NusaCrowd: Open Source Initiative for Indonesian NLP Resources
Year: 2023

Abstract:
We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments. NusaCrowd’s data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are underrepresented despite being widely spoken.

heading: 1 Introduction
text: Indonesia is one of the most linguistically diverse and populous countries in the world, with over 270 million people living across 18,000+ islands. It covers more than 700 spoken languages, making up ∼10% of all languages in the world (Grimes, 2000;
Lewis, 2009; Cohn and Ravindranath, 2014). However, the progress of NLP research in Indonesian languages has been held back by factors including language diversity (Anderbeck, 2008; Haryono, 2012; Siregar et al., 2014; Fauzi and Puspitorini, 2018), orthographic variation (Soeparno, 2015), resource limitation (Wilie et al., 2020; Koto et al., 2020b), and other societal challenges (Nurjanah, 2018; Jahang and Meirina, 2021; Aji et al., 2022).
Existing NLP research mainly focuses on highresource languages (Wang et al., 2018; Xu et al., 2020; Ruder, 2022), while the vast majority of
languages with limited data—including most languages spoken in Indonesia—are neglected (Joshi et al., 2020). Specifically, many Indonesian NLP resources are scattered, undocumented, and not publicly available. These issues cause a severe data scarcity problem, which hinders NLP research in Indonesian and other local languages spoken in Indonesia from progressing.
In this work, we introduce NusaCrowd,1 an open collaborative effort to gather and unify existing resources in Indonesian languages for public use, and liberate non-public resources. This initiative has successfully collected a total of 137 datasheets with 118 standardized data loaders in NusaCrowd Data Hub2. The datasets were manually assessed for data quality by multiple native speakers and experts in NLP. Utilizing the datasets collected in NusaCrowd, we introduce the first zero-shot NLU benchmark (NusaNLU), zero-shot NLG benchmark (NusaNLG), and multilingual ASR benchmark (NusaASR) for Indonesian languages. We evaluate various Indonesian and multilingual models on the benchmarks.
Our contributions can be summarized as follows: • We introduce the first large-scale resource hub
of standardized Indonesian corpora, covering 100+ datasets and 200+ tasks, spanning 19 Indonesian languages in text, speech, and image modalities. As part of this, we provide firsttime access to 14 previously private datasets.
• We develop the first Indonesian multilingual zero-shot benchmarks for natural language understanding (NusaNLU) and natural language generation (NusaNLG), which cover 40 NLU and NLG tasks in 12 languages.
• We conduct a comprehensive analysis of the collected datasets across various factors. Our analysis reflects the quality and diversity of existing NLP datasets in Indonesian and other languages spoken in the region.
• For speech, our initiative opens up access to a wide variety of ASR corpora (∼800 hours) covering 10 Indonesian languages. Using these resources, we build NusaASR and develop various Indonesian monolingual and
1NusaCrowd is a portmanteau of the words Nusantara and Crowd. The word Nusantara is derived from an old Javanese term referring to the territories of the Majapahit empire that corresponds to present-day Indonesia.
2We publicly release NusaCrowd’s data hub at https: //github.com/IndoNLP/nusa-crowd and the NusaCatalogue at https://indonlp.github.io/ nusa-catalogue/
multilingual ASR models.

heading: 2 Related Work
text: Indonesian NLP Resources The lack of labeled datasets for training and evaluation has impeded the advancement of NLP research in Indonesian languages (Aji et al., 2022). As a result, research has focused on using unlabeled data by building large language models (LLMs) to enable zero-shot and few-shot transfer learning. In recent years, multiple efforts have worked on language models (LMs) in Indonesian languages by exploring and developing different LM structures. Several efforts have focused on encoder-only LMs, such as IndoBERT (Wilie et al., 2020; Koto et al., 2020b), SundaBERT (Wongso et al., 2022), and IndoBERTTweet (Koto et al., 2021). Elsewhere, a number of generative models have been proposed, i.e., IndoBART and IndoGPT, along with the generation task benchmark, IndoNLG (Cahyawijaya et al., 2021b).
Open and Community-based Initiatives Open source/open science initiatives are a core part of the motivation behind this paper. Large-scale collaborations have made their mark in various research areas through developing a variety of resources, e.g., LMs (Scao et al., 2022; Muennighoff et al., 2022), datasets (Ardila et al., 2020; Adelani et al., 2021; Mager et al., 2021), catalogues (Alyafeai et al., 2022; Altaher et al., 2022; McMillan-Major et al., 2022), and benchmarks (Srivastava et al., 2022; Dhole et al., 2021; Fries et al., 2022).

heading: 3 NusaCrowd
text: In this section, we provide an overview of NusaCrowd, a detailed description of the NusaCrowd framework, the dataset curation process, as well as a detailed summary and statistics of the datasets contained in NusaCrowd.

heading: 3.1 Overview of NusaCrowd
text: NusaCrowd is a crowdsourcing initiative to collect, open-source, and standardize access to datasets in Indonesian and 700+ local languages in Indonesia. NusaCrowd aims to address the resource limitation problem in Indonesian NLP across three dimensions: (1) complete datasheets for each curated, ready-to-use dataset; (2) an open-access and centralized data hub for accessing datasets through standardized data loading scripts; and (3) promoting public data access for published non-public datasets. Through promoting public data access,
NusaCrowd provides access to 14 previously nonpublic datasets, some of which are multilingual, covering a total of ∼40 tasks over 12 languages. It also serves as a portal for retrieving and loading a wide variety of Indonesian NLP datasets, in text and other modalities (e.g., speech and images). NusaCrowd does not store or copy any of the hosted datasets, and control and ownership of the hosted datasets belong to the original owners.

heading: 3.2 NusaCrowd Framework
text: As shown in Figure 1, NusaCrowd consists of two platforms: NusaCatalogue and NusaCrowd Data Hub. The two platforms interact to support dataset registration and provide a standardized pipeline for NusaCrowd. In general, NusaCatalogue stores the datasheets (metadata) of all datasets, and NusaCrowd Data Hub stores the standardized data loaders for all of the datasets. The two systems share information about the datasheets and the data loaders, enabling users to seamlessly explore and use the datasets.3
NusaCrowd Workflow The dataset registration and standardization pipeline in NusaCrowd consists of four stages: (1) submission of datasheet information through an online form; (2) manual curation of the datasheet information by an expert in NLP, which, once approved (Section 3.3), is made available via the NusaCatalogue portal and a data loader implementation request is submitted to NusaCrowd Data Hub; (3) implementation of a data loader; and (4) review and approval of the implemented data loader by two maintainers, which is then published on NusaCrowd Data Hub. In addition to the datasheets, we also provide instructions
3All code in NusaCrowd will be made publicly available under Apache License 2.0.
on how to use the data in NusaCatalogue.

heading: 3.3 Dataset Standardization and Curation
text: We standardize the tasks from the datasets in NusaCrowd into several categories according to a specific schema, defined as a common set of attributes required to perform the task. We use the schema to cover similar tasks across the datasets. We define 13 schemas to cover all the tasks and modalities in the datasets, e.g., text classification, text generation, image captioning, and speech recognition. For instance, in the single-label text classification schema (TEXT), each example consists of three attributes (id, text, label), where id denotes a unique row identifier, text denotes the input text, and label denotes a discriminative target variable. We elaborate on the attributes of each schema in Appendix B.
To assess the quality of the datasets in NusaCrowd, we perform manual curation for each datasheet submission based on two criteria: language correctness, and the annotation process. We provide the results as metadata for each dataset. We check the correctness of the reported language using off-the-shelf language identification (LID) tools. We perform LID in 4 languages: English, Indonesian, Sundanese, and Javanese, We measure the LID accuracy compared to the reported languages in the metadata on all tasks containing text modality in NusaCrowd. Since many datasets consist of a large number of samples, language correctness checking is done both automatically and manually.
We conduct automatic language identification for 4 languages, i.e., English, Indonesian, Sundanese, and Javanese 5 using 3 off-the-shelf language identification tools, i.e., langid.py (Lui and
5We only perform language identification as these are the only languages supported by most of the existing off-the-shelf language identification tools.
Baldwin, 2012), FastText LID (Ooms, 2022), and Google CLD3 (Ooms, 2022). For other languages, since there is no language identification library available, the curation is done manually through sampling. Based on the automatic language identification result in Table 1, the correctness of languages is quite high, indicated by the top-3 accuracy of each language identification tools 6. Additionally, the accuracy of Indonesian is not as high as English, we conjecture that this is caused by there are many English terms from tasks that are collected from online platforms.
For assessing the annotation process for each dataset, we manually check the dataset annotation process from relevant publications and/or other descriptions and classify them into five categories, i.e., human-generated, crawling with human annotation, machine-generated with human curation, machine-generated or crawling without human curation, and unknown. The statistics of the dataset annotation assessment are shown in Appendix K.
6we don’t consider Top-1 for Sundanese and Javanese since the languages are low-resource and often mispredicted
In general, ∼90% of all the datasets listed in NusaCrowd are human-curated, showing that most of the datasets in NusaCrowd are high-quality and well-suited for building and evaluating Indonesian NLP models. Moreover, almost half of the datasets are collected through crawling and are annotated manually by humans, usually for NLP tasks such as sentiment analysis, emotion recognition, hate speech detection, named entity recognition, and machine translation. The crawling often comes from sources such as social media, news platforms, online reviews, etc.

heading: 3.4 Datasets in NusaCrowd
text: NusaCrowd includes 137 datasheets and 118 dataloaders, including access to 14 previously nonpublic datasets, and a variety of tasks and languages. We list all of the previously private datasets in Appendix I. NusaCrowd covers 36 task types, including: machine translation, summarization, sentiment analysis, part-of-speech (POS) tagging, and question answering, which are standardized into 13 different schemas. The datasets in
NusaCrowd stem from three modalities—image, text, and speech—with the majority of the data coming from the text modality. In terms of languages, NusaCrowd covers 19 Indonesian languages, i.e., Indonesian and 18 regional languages, in addition to some non-Indonesian languages such as Japanese, English, Spanish, and Russian, which come into the mix as machine translation language pairs. A summary of the datasets is shown in Figure 3. A list of language codes with the complete language name and family is provided in Appendix A. We present comprehensive details of the datasets in Appendix K, and a comparison of NusaCrowd with other initiatives in Appendix J.
Modalities NusaCrowd comprises datasets from three different modalities, i.e., image, text, and speech, all of which are related to language tasks. Most datasets contain text data for natural language understanding (e.g., sentiment analysis, named entity recognition, and parsing) and natural language generation tasks (e.g., machine translation, paraphrasing, and abstractive summarization). These account for 29 out of 36 task types in NusaCrowd. In addition, NusaCrowd covers three vision tasks: vision-language pre-training, image captioning, and text-to-image generation. For speech, NusaCrowd covers four tasks: automatic speech recognition (ASR), text-to-speech synthesis (TTS), speech-to-text translation (S2T), and speechto-speech translation (S2S).
Languages NusaCrowd covers Indonesian and 18 regional languages. Most languages covered in NusaCrowd belong to the Austronesian language family, 14 of which are part of Malayo-Polynesian family (including Indonesian), 2 of which are creole languages, i.e., Tok Pisin (tpi) and Tetun Dili (tdt).7 The other two languages — Hakka/Khek (hak) and Min Nan (nan) with Teochew dialect — are Sinitic and belong to the Sino-Tibetan language family. Detailed descriptions of each language are provided in Appendix A.

heading: 4 NusaCrowd Benchmarks
text: To showcase the benefit of NusaCrowd, we develop three different benchmarks from subsets of the datasets. Specifically, we develop benchmarks
6Based on ISO639-3 language codes: https://iso6 39-3.sil.org/code_tables/639/data.
7The two languages are not spoken in Indonesia, but instead used in neighboring countries: Papua New Guinea and Timor Leste.
for Indonesian and other local languages including a zero-shot NLU benchmark (NusaNLU), a zero-shot NLG benchmark (NusaNLG), and a multilingual ASR benchmark (NusaASR).

heading: 4.1 NusaNLU
text: Existing benchmarks (Wilie et al., 2020; Koto et al., 2020b) in Indonesian NLU only cover one language, i.e., the national language, Indonesian. Moreover, these benchmarks only focus on comparing traditional machine learning approaches with the fine-tuning approaches of pre-trained LMs. Following recent work in other high-resource languages that explore zero-shot generalization of large LMs (Scao et al., 2022; Lin et al., 2022; Muennighoff et al., 2022; Fries et al., 2022), we develop NusaNLU, the first zero-shot NLU benchmark in Indonesian and regional languages to benchmark zero-shot techniques over 26 datasets using both Indonesian monolingual and multilingual LMs. NusaNLU covers 12 languages across various tasks, including 3 emotion classification tasks (Saputri et al., 2018; Yulianti et al., 2021; Riccosan et al., 2022), 18 sentiment analysis tasks (Winata et al., 2023; Nurlaila et al., 2017; Hidayatullah et al., 2020; Wongso et al., 2021; Koto et al., 2020b; Purwarianti and Crisdayanti, 2019), one review score rating task8, one hate speech detection task (Ibrohim and Budi, 2019), one abusive language detection task (Putri et al., 2021), one next tweet prediction task (Koto et al., 2020b), and one natural language inference (NLI) task (Mahendra et al., 2021). A visual overview of the datasets in NusaNLU is provided in Figure 4.
Models We evaluate three state-of-the-art multilingual language models: XLM-R (Conneau et al., 2020), XGLM (Lin et al., 2022), and BLOOMZ (Muennighoff et al., 2022). We generally evaluate in a zero-shot cross-lingual transfer setting (Hu et al., 2020). For XLM-R, we employ intermediate-task training on NLI by predicting the entailment relation between the input text and the label (Phang et al., 2020). We explore both XLM-R fine-tuned on XNLI (Conneau et al., 2018) and Indonesian IndoNLI (Mahendra et al., 2021). For XGLM and BLOOMZ, we employ zero-shot prompt-based learning with prompts in English and Indonesian. For each language and task, we employ three different prompts and take the average score
8https://huggingface.co/datasets/jaka rtaresearch/google-play-review
for the evaluation of each task. More details about fine-tuning hyperparameters and the prompt used in the NLU experiments are shown in Appendix C.
Results Figure 4 shows the zero-shot NLU results of all the models. Overall, the prompting performance of BLOOMZ outperforms other models. Prompting with BLOOMZ outperforms XGLM by a huge margin, providing evidence of the benefit of instruction tuning for prompting. Interestingly, zero-shot cross-task transfer using XLM-R trained on XNLI (XLM-R XNLI) outperforms prompting using XGLM and performs on par with prompting using BLOOMZ, despite the huge difference in their model sizes. This suggests that large LMs are not always needed to perform zero-shot NLU tasks and better efficiency can be achieved through crosstask transfer using much smaller models while achieving similar performance.
Comparing the performance of cross-task finetuning across monolingual and multilingual NLI, XLM-R XNLI (122k training instances) outperforms XLM-R IndoNLI (11k training instances) by a large margin, suggesting that using largescale multilingual data is more effective than using smaller-scale data from closely-related or even the same language to fine-tune a multilingual model in a zero-shot cross-task setting. Comparing the language of the prompts, both BLOOMZ and XGLM with English prompts perform better than the corresponding models with Indonesian prompts. Our findings align with prior work (Muennighoff et al., 2022; Lin et al., 2022; Shi et al., 2022), which shows that, in most cases, the corresponding models perform better in English than on humantranslated prompts, despite the language distance between the prompt template and the corresponding text data.
Comparing the performance across different lan-
guages, as shown in Figure 5, we can conclude that the performance of all models is generally better for Indonesian and English compared to regional Indonesian languages, suggesting that existing multilingual models are unable to generalize well on these languages, and better language representations are vital to close the gap. A full breakdown of per-task performance is provided in Appendix F.

heading: 4.2 NusaNLG
text: Recent work on Indonesian NLG benchmarks (Cahyawijaya et al., 2021b; Guntara et al., 2020) has employed transformer-based models, both decoder-only (e.g., IndoGPT) and encoder–decoder (e.g., IndoBART) architectures. To further broaden NLG research in Indonesian and other regional languages, we develop an NLG benchmark, NusaNLG, which covers NLG tasks in 12 languages including English, Indonesian, and 10 local languages. NusaNLG incorporates a total of 36 datasets across various tasks covering 33 machine translation tasks (Guntara et al., 2020;
Cahyawijaya et al., 2021b) and 3 summarization tasks (Kurniawan and Louvan, 2018; Koto et al., 2020a) (Figure 6). We use SacreBLEU for machine translation evaluation, and ROUGE-L for summarization evaluation.
Models Following recent work in prompting, we explore the possibility of zero-shot generalization of various large LMs on generation tasks through prompting on two NLG tasks, i.e., machine translation and summarization. To explore the effect of different prompt languages on the zero-shot generalization performance, we evaluate prompts in English and Indonesian. We employ two large LMs: XGLM (Lin et al., 2022), and BLOOMZ (Muennighoff et al., 2022). For each task and prompt language, we provide three different prompts and average the result. More details about the hyperparameters and the prompt used in the NLG experiments are shown in Appendix D.
Results The zero-shot NLG results of all models are shown in Figure 6. Outputs obtained by prompting BLOOMZ outperform those obtained from XGLM for both English and Indonesian prompts. The performance is better on average when prompting BLOOMZ with English prompts than when using Indonesian prompts, which aligns with the results of BLOOMZ on XNLI (Conneau et al., 2018), where BLOOMZ with English prompts performs better than the human-translated prompts (Muennighoff et al., 2022).
Prompting using XGLM yields better quality outputs using Indonesian language prompts than using English prompts. A similar result is reported in XGLM evaluation for Spanish XNLI and Chinese XCOPA (Ponti et al., 2020), which shows that prompting with the human-translated prompt to the target language produces a better score than the En-
glish one. For the BLOOMZ models, the result for English is better since we use the BLOOMZ checkpoint fine-tuned only on English prompts. Additionally, we found that the zero-shot translation quality across all models and prompt languages is poor, especially for local languages, as shown in Table 2. This is even more severe when local languages are involved, yielding ∼2% SacreBLEU. This finding suggests that existing large multilingual LMs still fail to learn representations for these local languages. A full breakdown of per-task results over NusaNLG is provided in Appendix G.

heading: 4.3 NusaASR
text: In addition to zero-shot benchmarks for textual language data, we showcase the benefit of NusaCrowd by extending the NLP benchmark in Indonesian languages to speech. We develop the first multilingual ASR benchmark for Indonesian and other local languages covering 17 ASR datasets in eight languages: 5×Indonesian (ind), 3×Sundanese (sun), 3×Javanese (jav), 2×Balinese (ban), 1×Acehnese (ace), 1×Batak (btk), 1×Buginese (bug), and 1×Minangkabau (min).
Models We employ pre-trained wav2vec 2.0 (Baevski et al., 2020) models in our experiment. We explore three training settings: single-task
monolingual multi-task training, Highlight denotes that the model is trained in the corresponding language.
monolingual training, where we fine-tune and evaluate the model on the corresponding ASR dataset; multi-task monolingual training, where we fine-tune the model using multiple ASR datasets on a single language (we evaluate three languages with the largest resources, i.e., Indonesian, Javanese, and Sundanese); and joint multi-task multilingual training, where we fine-tune the model using all 17 ASR datasets listed on NusaASR. We experiment with two wav2vec 2.0LARGE (∼300M parameters) checkpoints, i.e., an unsupervised pre-trained XLS-R wav2vec 2.0 (wav2vec 2.0-pt)9 and an Indonesian, Javanese, and Sundanese ASR fine-tuned XLS-R wav2vec 2.0 (wav2vec 2.0-ft).10 In addition to wav2vec 2.0, we also employ WhisperSMALL (Radford et al., 2022)11 (∼250M parameters). Full details of the experiment setup are provided in Appendix E.
Results Table 3 shows the per-language taskaveraged performances of wav2vec 2.0 models over NusaASR. The complete per-task results of NusaASR along with the performance
9https://huggingface.co/facebook/wav2 vec2-large-xlsr-53
10https://huggingface.co/indonesian-nlp /wav2vec2-indonesian-javanese-sundanese
11https://huggingface.co/openai/whispe r-small
of WhisperSMALL are provided in Appendix H. Based on the results, single-task training on wav2vec 2.0-pt performs poorly due to the limited training data to adapt from unsupervised contrastive pre-training to the ASR task, while the ASR fine-tuned wav2vec 2.0-ft model yields decent results in most languages, except for Buginese (bug) with 90.09% WER. This suggests limited transferability from Indonesian, Sundanese, and Javanese to Buginese, consistent with the analysis from NusaX (Winata et al., 2023) regarding the low overlap between Buginese and other local languages included in NusaCrowd. For monolingual multi-task training, all models perform well only in the languages that they were trained on. This shows that there is a large difference between vocabulary and speech features from one language to another.
For all models evaluated over NusaASR (wav2vec 2.0-pt, wav2vec 2.0-ft, and Whisper), the best performance is achieved through multilingual multi-task training, yielding as low as ∼20% average WER across all languages, suggesting transferability of speech features from one language to the others (Fung et al., 1998; PLU et al., 2000; Sakti et al., 2012; Nakayama et al., 2019). Unlike prior work (Winata et al., 2023), where Acehnese (ace) yields similar performance to other languages in sentiment analysis, the same behavior
is not reflected in ASR. This suggests that there is a distinction between the speech of Acehnese (ace) and other regional languages, despite vocabulary overlap and shared language structure.

heading: 5 Discussion
text: Multilinguality for Low-Resource Languages Despite the higher pre-training cost relative to monolingual LMs (Cahyawijaya et al., 2021b), multilingual LMs are more versatile and transferable. Recent low-resource monolingual language LMs are on the scale of a hundred million parameters, while the size of multilingual LMs, within a period of three years, has increased by around 1,000× from ∼100M to ≥100B parameters (Devlin et al., 2019; Xue et al., 2021; Tang et al., 2021; Muennighoff et al., 2022; Scao et al., 2022). This benefit comes from the data scale of multilingual LMs, which is orders of magnitude larger than monolingual LMs. Additionally, multilingual LMs benefit from positive transfer between related languages, which is especially beneficial for low-resource languages. Moving forward, we expect that multilingual LMs will play a significant role in the exploration of low-resource languages.
Viability of Large Models for Indonesian Computational resources are limited among Indonesian research institutions and in industry, even among the top Indonesian universities (Indonesia, 2020; Nityasya et al., 2020). Focusing solely on large LMs will limit accessibility, and adoption will likely be low. Therefore, although larger LMs empirically offer better quality, we instead suggest investing more effort in efficiency. This includes smaller sizes LMs and modularized LMs (Pfeiffer et al., 2020; Ansell et al., 2021; Pfeiffer et al., 2022). Furthermore, more work on efficiency through factorization (Winata et al., 2020; Cahyawijaya et al., 2021a), pruning (Frankle and Carbin, 2019; Dai et al., 2021), quantization (Shen et al., 2020; Aji and Heafield, 2020), or distillation (Zhang et al., 2020; Bai et al., 2021; Dai et al., 2022) are also likely to be beneficial.

heading: 6 Conclusion
text: We have introduced NusaCrowd, a combined resource for Indonesian and regional languages, covering 137 datasets, 118 of which have a standardized loader. NusaCrowd covers Indonesian and 18 regional languages, encompassing 3 different data
modalities. Manual and automatic curation processes were conducted to verify the quality of the collected datasets. The effectiveness of NusaCrowd is shown in three use cases: zero-shot NLU (NusaNLU), zero-shot NLG (NusaNLG), and multilingual ASR (NusaASR) benchmarks. Our experiments provide evidence regarding the efficiency of cross-tasks method over prompting for zero-shot NLU, the limited capabilities of existing large LMs for handling NLG tasks in local languages, and the potential of joint multilingual multi-task learning for Indonesian ASR. We hope NusaCrowd will benefit the research community as a data hub for Indonesian and regional languages by facilitating easy access to datasets, as well as accelerating research and development.

heading: 7 Limitations
text: Dataset Utilization We have collected 137 datasets, yet we have only conducted experiments over a minority of these (∼40 datasets), leaving the remaining datasets unexplored. Since the datasets are already curated, future work should further explore these datasets in additional experiments. In this work, we do not experiment on image-text datasets for two reasons: (1) all of the image-text datasets are translated from English versions; and (2) there is no large LM available for zero-shot image-to-text generation.
Experiments We did not attempt few-shot or fully-supervised learning experiments in NusaCrowd since prior work has explored these approaches on some of the datasets (Wilie et al., 2020; Koto et al., 2020b; Cahyawijaya et al., 2021b; Winata et al., 2023). We specifically conduct our experiments on zero-shot methods to explore the generalization of zero-shot cross-lingual and zero-shot prompting approaches to extremely lowresource languages.
Task Diversity The tasks represented in NusaCrowd are skewed towards MT, sentiment, abusive text classification, and ASR. Many other tasks remain unexplored for Indonesian and regional languages. Furthermore, most ASR work come from the same authors or research groups. While these topics are prevalent among Indonesian researchers, it is also important to expand to other tasks.
Domain Diversity The datasets in NusaCrowd are primarily from the domains of social media,
news, and other general domain sources. Despite having a huge potential, narrow-domain datasets, such as clinical, biomedical, legal, financial, and educational datasets remain underrepresented for Indonesian and regional languages. Exploration of domain-specific data and use cases for Indonesian and regional languages is critical.
Language Diversity There are 700+ languages in Indonesia. However, we have only focused on a small fraction of these languages. In addition, there are also other regional languages similar to the two Sinitic languages in NusaCrowd, i.e., Hakka (Khek) and Min Nan (Teochew). More focus on under-represented languages is an interesting future direction.
Multimodality The datasets in NusaCrowd are mainly in the text modality. Exploration of speech, image, and other modalities for Indonesian and regional languages is still limited, and there are potentially exciting opportunities to capture locallyrelevant Indonesian culture in such modalities.
Utilization of Datasets There are 137 datasets contained in NusaCrowd. While we showcased three different use cases for the datasets (i.e., zeroshot NLU, zero-shot NLG, and multilingual ASR benchmarks), there is much greater potential to use the datasets in NusaCrowd. Potential areas of focus include experimenting with various approaches and analyses over multiple datasets, such as multi-task learning, continual learning, or few-shot learning.

heading: 8 Ethical Statement
text: Our work highlights the importance of democratizing access to Natural Language Processing (NLP) technology for underrepresented and extremely low-resource languages with a focus on the Austronesian language family specifically in Indonesian languages. Within our study, we are well aware of the ethical responsibility associated with language research and the potential impact that comes with it. Our study prioritizes diversity, inclusivity, and fairness. Within this work, the contribution of each collaborator is calculated following a fair and transparent scoring guideline that empowers the core principles of NusaCrowd. We have obtained informed consent from all dataset authors to provide publicly open-access corpora and benchmarks. Throughout our research process, we have made conscious efforts to engage with the language communities, involve local experts, and
respect their linguistic and cultural nuances. We encourage further collaboration and engagement with underrepresented language communities to ensure that their voices are heard and their needs are addressed in future NLP development. We remain committed to the principles of ethical research, diversity, inclusivity, and fairness, striving to promote social good through our work in the field of language technology.

heading: A Languages in NusaCrowd
text: Table A provides the language code, name, and family for all 19 languages listed in NusaCrowd. The language family information is collected from Ethnologue (Eberhard et al., 2021). We follow the ISO 639-3 standard12 for language coding in NusaCrowd. The language tree of all languages in NusaCrowd is shown in Figure A.
Acehnese (ace) is a language spoken mainly in the Aceh province. Although it is the de facto language of provincial identity of Aceh, language use is shifting to Indonesian in urban areas. Acehnese has features typical of the Mon-Khmer languages of mainland Southeast Asia, a result of its former status as part of the early Chamic dialect continuum on the coast of Vietnam. It has at least ten contrasting vowels and as many distinct diphthongs, as well as voiceless aspirated stops and murmured voiced stops (Blust, 2013). In addition to the large number of diphthongs, it has a high percentage of monosyllabic root morphemes. Prefixes and infixes play an active role while suffixes are absent (Durie, 1985). It is of the ‘active’ or so-called ‘Split-S’ type: some intransitive verbs take argu-
12https://iso639-3.sil.org/
Figure A: Language family tree for all the languages covered in NusaCrowd. Most languages are Austronesian with two Creole languages and two other languages are under Sino-Tibetan language family.
ments, which have the properties of ‘transitive subjects’ while others take arguments with the properties of ‘transitive objects’ (Durie, 1988).
Lampung Nyo (abl) is a language spoken in three enclaves east between Kanan and Seputih rivers in Lampung province. It is one of the three languages under the subgroup Lampung. The other two languages are Komering and Lampung Api. It has four dialects: Abung, Tulangbawang, Sukadana, and Melinting, with 77% of lexical similarity among dialects. It was written in Kaganga script but it is written mainly in Latin script (Eberhard et al., 2021).
Balinese (ban) is a language spoken mainly in the Bali province and in the West Nusa Tenggara province. It has three main dialects: Highland Balinese, Lowland Balinese, and Nusa Penida. It is mainly written in the Latin script since the early 20th century although it has its own Balinese script. The word order in Balinese is SVO. It is non-tonal and has 17 consonant and 6 vowel phonemes. Stress is on the penultimate syllable. It has three sociolinguistic registers. Regarding patterns of verb affixation, Balinese is an ‘active’ or ‘split-S’ language: verbs with Undergoer-like
subject arguments are marked in one way (with a ‘zero prefix’), while verbs with Actor-like subject arguments—intransitive or transitive—are marked in another (either with the nasal prefix ‘N-’, or with ‘ma-’) (Arka, 2003).
Toba Batak (bbc) is a language spoken in the North Sumatra province. Similarly to Acehnese, it is slowly being replaced by Indonesian in urban and migrant areas. It used to be written in the Batak script but is mainly written in Latin script now. The Batak languages are predicate-initial, and have verb systems reminiscent of Philippine languages, although they differ from them in many details (Blust, 2013).
Banjarese (bjn) is a language spoken in Kalimantan (Central, East, South, and West Kalimantan provinces). It became a language of wider communication through trade in the market, in business, and in media. It is dominant in the South Kalimantan Province and also growing rapidly in the Central and Eastern Kalimantan provinces. It has two main dialects: Kuala and Hulu dialects. Although it is a Malayic language, it has many Javanese loanwords, probably acquired during the Majapahit period from the late thirteenth century until the fifteenth century (Blust, 2013). It has 73% of lexical similarity with Indonesian13 and it is written in Arabic and Latin scripts.
Batak languages (btk) are a subgroup of the languages of Northwest Sumatra-Barrier Islands spoken by the Batak people in the North Sumatra province and surrounding areas. Batak languages can be divided into three groups: Northern, Simalungan, and Southern. The Northern group consists of three languages: Batak AlasKluet (btz), Batak Dairi (btd), and Batak Karo (btx). The Simalungan group has one language only, i.e., Batak Simalungun (bts). The Southern group consists of three languages: Batak Angkola (akb), Batak Mandailing (btm), and Batak Toba (bbc) (Eberhard et al., 2021). The Batak languages were written using the Batak script, but the Latin script is now used for most writing.
Batak Karo (btx) is a language spoken in Aceh province and North Sumatra province. The language status is threatened. The lexical similarity is 81% with Batak Dairi (btd), 80% with Batak Simalungun (bts), and 76% with Batak Alas-Kluet (btz) (Woollams, 2005). It has 17 consonants and 7 vowels. The stress is on the penultimate syllable.
13i.e., 73% of its words also occur in Indonesian.
Similar to Indonesian, it has inclusive/exclusive pronouns. The basic word order is SVO with prepositions. It is a head initial language, except for the order of quantifiers. It has two voices: actor-voice and undergoer-voice. It is written in Batak script and also Latin script.
Buginese (bug) is a language spoken mainly in the South Sulawesi, Southeast Sulawesi, Central Sulawesi, and West Sulawesi provinces. The word order is SVO. Verb affixes are used to mark persons. It is non-tonal and has 19 consonant and 6 vowel phonemes. Stress is on the penultimate syllable. It was written in the Buginese script in the past (derived from Brahmi script) but is mainly written in Latin script now (Eberhard et al., 2021). In Buginese, the pronoun ‘I’ has three forms: the independent form ‘iyya’, the ergative form ‘-ka’, and the absolutive form/clitic ‘u-’. Buginese employs sentence patterns, pronouns, and certain terms to express politeness (Weda, 2016).
Hakka (hak) is a language spoken in Southeastern China, mainly in Guangdong province, also in Fujian, Guangxi, Hainan, Hunan, south Jiangxi, and Sichuan provinces. It is also spoken by Chinese descendants in some parts in Indonesia, such as in Singkawang in West Kalimantan province (Stenberg, 2015), in Medan in North Sumatra province (Nasution and Ayuningtyas, 2020), and in Lhokseumawe in Aceh province (Saleh et al., 2018). It is a tonal language and the basic word order is SVO. It is written in Han script and also Latin script.
Indonesian (ind) is the national language of Indonesia in 1945 Constitution, Article 36. Its lexical similarity to Standard Malay is over 80%. The word order is SVO. It is non-tonal and has 19 consonants, 6 vowels, and 3 diphthongs. The stress is on the penultimate syllable. It has a rich affixation system, including a variety of prefixes, suffixes, circumfixes, and reduplication. Most of the affixes in Indonesian are derivational (Pisceldo et al., 2008). It is developed from literary ‘Classical Malay’ of the Riau-Johor sultanate (Sneddon, 2003) and has regional variants. It is written in Latin script.
Javanese (jav) is a language spoken mainly in Java island. It is the de facto language of provincial identity in central and eastern Java. The word order is SVO. It has 21 consonants and 8 vowels. It used to be written in Javanese script but since 20th century is mostly written in Latin script. Javanese differs from most other languages of western In-
donesia in contrasting dental and retroflex stops, and in the feature of breathy voice or murmur as a phonetic property of its voiced obstruents. Javanese also differs from most languages of the Philippines and western Indonesia in allowing a number of word-initial consonant clusters. It has an elaborate system of speech levels (Blust, 2013).
Madurese (mad) is a language spoken in the East Java province, mainly on Madura Island, south and west of Surabaya city, Bawean, Kangean, and Sapudi islands. It has vowel harmony, gemination, rich affixation, three types of reduplication, and SVO basic word order (Davies, 2010).
Minangkabau (min) is a language spoken mainly in West Sumatra and other provinces on Sumatra Island such as Bengkulu and Riau. Although it is classified as Malay, it is not intelligible with Indonesian. The word order is SVO written in Latin script. Standard Minangkabau voice can be characterised as an Indonesian-type system whereas colloquial Minangkabau voice is more effectively characterised as a Sundic-type system (Crouch, 2009).
Min Nan (nan) is a language spoken in Southeastern China. One of its dialects is ChaozhouShantou (Chao-Shan dialect) or Teochew dialect. It is spoken by Chinese descendants in some parts of Indonesia such as in Jambi (Peng, 2011) and in Pontianak in West Kalimantan province (Veniranda, 2015). While Teochew is historically Chinese, its contact with languages in Indonesia has resulted in some changes uncharacteristic of Chinese languages. For example, regarding word order, Teochew spoken in Jambi exhibits both headfinal and head-initial relative clauses even though head-initial relative clauses are generally ungrammatical in Chinese languages. In addition to the head-initial word order, Jambi Teochew has also borrowed the Malay relativizer yang (Peng, 2011). It is a tonal language with tone sandhi. The word order is SVO (Eberhard et al., 2021).
Ngaju (nij) is a language spoken in the Central Kalimantan province. It is widely used as a language of wider communication for trade in much of Kalimantan, from the Barito to the Sampit river. It is used in many domains (church, school, villagelevel government, market, etc.). It has various affixes and reduplication, similar to Indonesian. The active voice is marked by prefix ‘maN-’ and the passive voice is marked by prefix ‘iN-’. The word order is similar to the one in Indonesian. The pro-
nouns have enclitic forms to mark possessors in a noun phrase or agents in a passive sentence (Uchibori and Shibata, 1988).
Sundanese (sun) is a language spoken mainly in the Banten and West Java provinces. It is the de facto language of provincial identity in western Java. The main dialects are Bogor (Krawang), Pringan, and Cirebon. It is non-tonal and has 18 consonant and 7 vowel phonemes. The stress is on the penultimate syllable. It has elaborate coding of respect levels. It is written in Latin script since the middle of the 19th century but was previously written in Arabic, Javanese, and Sundanese scripts. Sundanese is a predominantly SVO language. It has voice marking and incorporates some (optional) actor-verb agreement, i.e., number and person (Kurniawan, 2013).
Tok Pisin (tpi) is an English-based creole and de facto the national language of Papua New Guinea, a neighboring country of Indonesia. Dialect differences exist among lowlands, highlands, and islands. Highlands lexicon has more English influence. It is a non-tonal language and has 16 consonant and 5 vowel phonemes. It has inclusive/exclusive pronouns and the basic word order is SVO. It is written in Latin script (Eberhard et al., 2021).
Tetun Dili (tdt) is a Tetun-based creole spoken in Dili district, East Timor north coast as the first language and scattered in western part of East Timor as the second language. It is a statutory national language according to the 2002 Constitution, Article 13. It has heavy Portuguese (por) and Mambae (mgm) influence as well as some Indonesian (ind) or Malay influence. It is a non-tonal language with 22 consonants and 5 vowels. The stress is most commonly on the penultimate syllable. It has inclusive/exclusive pronouns. The basic word order is SVO with prepositions and tense-aspect markers. It is a head-initial language, except for possessors. The speakers of Tetun Dili also use Tetun [tet], some bilingually, but many others have significant difficulty understanding it in many domains. It is written in Latin script (Eberhard et al., 2021).
Malayic Dayak (xdy) is a language widely dispersed in Central and West Kalimantan provinces. It has many dialects and it is written in Latin script (Eberhard et al., 2021). Malayic Dayak is not a proper subgroup, but refers to the large number of unclassified but clearly Malayic languages of Borneo which have a three voice system (Sommerlot, 2020).

heading: B Schemas in NusaCrowd
text: Schema serves to define and format the attributes of the dataset returned by a data loader. For each data loader, we implement a source schema, which is responsible to present the dataset in a format similar to its original structure, and a nusantara schema, which supports the standardization data structure across similar tasks.
We define the nusantara schemas as follows. Labels are in string format unless indicated otherwise.
• Image-text (IMTEXT). This schema could be used for image captioning, text-to-image generation, and vision-language pre-training. It consists of (id, text, image_paths, metadata), where id denotes a unique row identifier of the dataset, text denotes an input text, image_paths denotes a list of paths to the input image sources, and metadata denotes relevant details such as visual concepts and labels (if required). • Speech-text (SPTEXT). This could be used for speech recognition, text-to-speech (TTS) or speech synthesis, and speech-to-text translation. It consists of (id, path, audio, text, speaker_id, metadata), where id denotes a unique row identifier of the dataset, path denotes the file path to an input audio source, audio denotes the audio data loaded from the corresponding path, text denotes an input text, speaker_id denotes a unique identifier of the speaker, metadata denotes relevant details such as the age and gender of the speaker (if required). • Speech-to-speech (S2S). This could be used for speech-to-speech translation. It consists of (id, path_1, audio_1, text_1, metadata_1, path_2, audio_2, text_2, metadata_2), where id denotes a unique row identifier of the dataset, path_1 and path_2 denote the file path to a respective input audio source, audio_1 and audio_2 denote the audio data loaded from the corresponding path, text_1 and text_2 denote input texts, and metadata_1 and metadata_2 denote relevant details such as the age of the speaker and their gender (if required). • Unlabeled text (SSP). This schema could be used for language modeling in self-supervised pre-training. It consists of (id, text),
where id denotes a unique row identifier of the dataset and text denotes an input text.
• Single-label text classification (TEXT). This schema could be used for sentiment analysis, emotion classification, legal classification, and others. It consists of (id, text, label), where id denotes a unique row identifier of the dataset, text denotes an input text, and label denotes a deterministic target variable. • Multi-label text classification (TEXT MULTI). This schema could be used for hate speech detection and aspect-based sentiment analysis. It consists of (id, text, labels), where id denotes a unique row identifier of the dataset, text denotes an input text, and labels denotes a list of deterministic target variables. • Text-to-text (T2T). This schema could be used for machine translation, summarization, and paraphrasing. It consists of (id, text_1, text_2, text_1_name, text_2_name), where id denotes a unique row identifier of the dataset, text_1 and text_2 denote an input text pair, and text_1_name and text_2_name denote the names of the input text pair (e.g., ind and jav for translation input text pairs, or document and summary for summarization input text pairs). • Sequence labeling (SEQ LABEL). This schema could be used for named entity recognition (NER), POS tagging, and others. It consists of (id, tokens, labels), where id denotes a unique row identifier of the dataset, tokens denotes a list of tokens of an input text, and labels denotes a list of targets for the tokens. • Question answering (QA). This schema could be used for extractive QA, multiplechoice QA, and others. It consists of (id, question_id, document_id, question, type, choices, context, answer), where id denotes a unique row identifier of the dataset, question_id denotes a unique identifier of the question, document_id denotes a unique identifier of the context document, question denotes an input question to be answered, type denotes the type of the QA task (e.g., extractive, multiple-choice,
open-generative, closed-generative, etc.), choices denotes a list of answer choices (if required), context denotes a passage that serves as the background information of the question (if required), and answer denotes the gold answer to the question (if required). • Single-label text pair classification (PAIRS). This could be used for textual entailment and next sentence prediction. It consists of (id, text_1, text_2, label), where id denotes a unique row identifier of the dataset, text_1 and text_2 denote an input text pair, and label denotes the target variable. • Single-label text pair classification with continuous values or regression (PAIRS SCORE). This could be used for answer grading and semantic textual similarity. It consists of (id, text_1, text_2, label), where id denotes a unique row identifier of the dataset, text_1 and text_2 denote an input text pair, and label denotes a target variable as a continuous value. • Multi-label text pair classification (PAIRS MULTI). This could be used for morphological inflection. It consists of (id, text_1, text_2, labels), where id denotes a unique row identifier of the dataset, text_1 and text_2 denote an input text pair, and labels denotes a list of target variables. • Knowledge base (KB). This schema could be used for constituency parsing, dependency parsing, coreference resolution, dialogue system, and other tasks with complex structures. It consists of (id, passages, entities, events, coreferences, relations). Considering its intricate structure, we encourage readers to take a look at the implementation of the knowledge base schema.

heading: C Details for Zero-Shot Setting Experiment in NusaNLU
text: Model Checkpoints For the NLU experiment, we utilize 4 model checkpoints, which are: 1) BLOOMZ fine-tuned on English prompt with 3B parameters14, 2) XGLM with 2.9B parameters15,
14https://huggingface.co/bigscience/bl oomz
15https://huggingface.co/facebook/xglm -2.9B
3) off-the-shelf XLM-R fine-tuned on XNLI16, and 4) XLM-R large fine-tuned on IndoNLI. For XLMR large fine-tuned on IndoNLI, we fine-tuned the XLM-R large model with batch size of 128 and initial learning rate of 1e-5 for 50 epochs. We use AdamW optimizer with a linear learning rate decay and apply early stopping of 5 epochs based on the validation accuracy score.
Prompts We run the prompting experiment using 3 different prompts for each task type. We cover several different task types in our NLG experiments, i.e., sentiment analysis, abusive detection, hate speech detection, emotion classification, natural language inference (NLI), and next tweet prediction. The prompt templates used for each task type are shown in Tables A and A.

heading: D Details for Zero-Shot Setting Experiment in NusaNLG
text: Model Checkpoints For the NLG experiment, we utilize 2 model checkpoints, i.e., BLOOMZ fine-tuned on English prompt with 3B parameters and XGLM with 2.9B parameters. We use the same checkpoint as the one used in the zero-shot NLU experiment.
Generation Hyperparameters For generating the prediction sequence, we generate sequence using greedy decoding with sampling, using top-k of 50 and top-p of 1.0. We force the model to at least generate one token and limit the generation sequence length to 100 tokens.
Prompts We run the prompting experiment using 3 different prompts for each task type. We cover two different task types in our NLG experiments, i.e., machine translation and summarization. The prompt templates used in our NLG experiment are shown in Table A and Table A.

heading: E Details of Speech Recognition Experiment in NusaASR
text: Model Checkpoints For both the monolingual and multilingual ASR experiment, we employ 2 wav2vec 2.0LARGE model checkpoints (both with ∼300M parameters) as follows: 1) pre-trained XLSR wav2vec 2.0 model17 and an off-the-shelf fine-tuned XLSR wav2vec 2.0 model to Indoensian,
16https://huggingface.co/joeddav/xlm-r oberta-large-xnli
17wav2vec2-large-xlsr-53:https://huggin gface.co/facebook/wav2vec2-large-xlsr-53
Sundanese, and Javanese speech data18. For Whisper model we employ the WhisperSMALL19 model with 244M parameters. For the monolingual experiment, we explore training using the 3 largest and widely-used languages in Indonesia, i.e., Indonesian (ind), Javanese (jav), and Sundanese (sun).
Fine-Tuning Hyperparameters We apply finetuning to both XLSR wav2vec 2.0 and Whisper models for single-task training, monolingual multitask training, and multilingual multi-task training settings. We fine-tune the models using the following hyperparameters, i.e., Adam optimizer with a learning rate of 5e-5 for the wav2vec 2.0 model and 1e-4 for the Whisper model, training batch size of 16, fine-tuning epoch of 30, and apply an early stopping of 5 epoch based on the validation word error rate (WER). For each model, we search for the best learning rate ranging from [5e-4 . . . 1e-5]. We run all experiments on a single A100 GPU.

heading: F Zero-Shot Results of NusaNLU
text: Here we elaborate further on the analysis in Section 4.1. We report the overall performances of each model in Figure A and per task performance in Table A. Predictions derived by prompting BLOOMZ outperform all the other models and perform on average on par with zero-shot crosstask prompting using the XLM-R model trained on XNLI. In detail, predictions using cross-task prompting actually are better in F1 than using BLOOMZ in 17 tasks, while it’s actually worse in accuracy in 13 tasks, all out of the 26 NLU tasks sampled. One extreme example can be observed in their performance comparison on the id_abusive task, where predicting by cross-task prompting XLM-R trained on XNLI nearly triples the F1 on prompting BLOOMZ. These results suggest that methods like cross-task prompting are worth exploring, benefitting better efficiency through crosstask transfer on low-resource language tasks compared to large multilingual LMs.
Comparing the languages of the prompt, although on both XGLM and BLOOMZ it’s better to use the English prompt, the difference is actually more apparent on average when prompting is done using XGLM. However, when we zoom into each of the tasks, the difference is much larger
18https://huggingface.co/indonesian-nlp /wav2vec2-indonesian-javanese-sundanese
19https://huggingface.co/openai/whispe r-small
in prompting using BLOOMZ. The largest spread is observed on utilizing the English prompt when predicting for the indolem sentiment analysis task, where the accuracy differs by ∼30%, and the F1 differs by ∼37.8%. Comparing the same variables in XGLM, the largest accuracy difference of ∼24% is observed on id_google_play_review_posneg, and the largest F1 difference of ∼19.1% is observed on Madurese (mad) sentiment analysis task. Furthermore, utilizing Indonesian prompts is not always the case, worse. On Buginese (bug) sentiment analysis utilizing BLOOMZ we can get ∼23% more accuracy by using Indonesian prompt. On classifying emotion in emotcmt task utilizing XGLM, we can get ∼7% more F1 by using also the Indonesian prompt. On the indolem next-tweet-prediction task, utilizing both BLOOMZ and XGLM using also the Indonesian prompt, we can get additional ∼14% accuracy and ∼23% F1 respectively.

heading: G Zero-Shot Results of NusaNLG
text: Here we elaborate further on the analysis in Section 4.2. We report the overall performances of each model in Figure A and per task performance in Table A. Generations derived by prompting BLOOMZ are better than prompting XGLM in all of the tasks except in indosum_fold0_nusantara_t2t, where the scores differ slightly. The performances in the summarization tasks are generally lower than the performances in the machine translation tasks. On the machine translation tasks, the performance in translating to the Indonesian language as the target language is generally higher than translating to the local languages, while translating from English to Indonesian is generally performing the highest.
Prompting using BLOOMZ yields better performances in most of the tasks, when prompting using English prompts than using Indonesian prompts. In general, prompting using XGLM yields better generation using Indonesian prompts than using English prompts. This is especially the case in the machine translation tasks, where most of them yield better performances except when translating to Toba Batak (bbc) and Banjarese (bjn) from Indonesian (ind), and also when translating to Minangkabau (min) to Indonesia (ind) and vice versa. In the summarization task, prompting using XGLM with English prompts produce better results than with Indonesian prompts.
It’s worth noting that the translation quality is
extremely poor for local languages, especially in Banjarese (bjn), Acehnese (ace), Toba Batak (bbc), Ngaju (nij), Madurese (mad), and Sundanese (sun). This is even more severe when those local languages become the target languages. This finding suggests that both BLOOMZ and XGLM still fail to learn the representation of these local languages.

heading: H ASR Results of NusaASR
text: Here we elaborate further on the analysis in Section 4.3. We report the per-task performance of each model in Table A for the wav2vec 2.0-pt model, Table A for the wav2vec 2.0 model-ft, and Table A for the Whisper model. The best overall performance is achieved by wav2vec 2.0-pt finetuned in multilingual multi-task setting, achieving 17.03% average WER over all tasks. The model also performs better in most cases for languages other than Indonesian compared to the wav2vec 2.0-ft model. While for Indonesian, wav2vec 2.0- ft fine-tuned in all 3 training settings, i.e., multilingual multi-task, monolingual (ind) multi-task, and single-task settings, achieve much better scores, i.e., <5% WER over all Indonesian tasks.
Comparing the performance per language, the best Indonesian ASR model achieves very low WER on Indonesian (ind) speech corpora, i.e., indspeech_digit_cdsr, indspeech_news_lvcsr, indspeech_teldialog_lvcsr, and indspeech_teldialog_svcsr. Compared to local languages, i.e., Minangkabau (min), Sundanese (sun), Javanese (jav), Balinese (ban), Acehnese (ace), Batak (btk), and Buginese (bug), the performance of the best ASR model only achieves ∼10-30% WER. The performance is especially low for Buginese (bug), which suggests distinct speech features are required for handling speech recognition in Buginese (bug) language. This fact aligns with the result of prior work in Indonesian local languages (Winata et al., 2023), where Buginese (bug) has inferior performance in the leave-one-language-out setting.

heading: I Private Datasets in NusaCrowd
text: NusaCrowd offers access to 14 previously private datasets. We provide the details of all 14 previously private datasets listed in NusaCrowd along with the task, languages, and modality in Table A.

heading: J Comparison with Other Initiatives
text: To provide a broader perspective of the impact of NusaCrowd, we provide the comparison of NusaCrowd initiatives with other global, regional, and Indonesian data initiatives in Table A.

heading: K Details of Datasets in NusaCrowd
text: Table A provides the details description, license, languages, dataset volume, annotation quality, and other metadata of all 137 datasets collected in NusaCrowd.
Language Prompt in Sentiment Analysis Task
Indonesian (ind)
[INPUT]\nApakah sentimen dari teks tersebut? [LABELS_CHOICE]
Apakah sentimen dari teks berikut?\nTeks: [INPUT]\nSentimen: [LABELS_CHOICE]
Teks: [INPUT]\n\nTolong prediksikan sentimen dari teks diatas: [LABELS_CHOICE]
English (eng)
[INPUT]\nWhat would be the sentiment of the text above? [LABELS_CHOICE]
What is the sentiment of this text?\nText: [INPUT]\nSentiment: [LABELS_CHOICE]
Text: [INPUT]\n\nPlease classify the sentiment of above text: [LABELS_CHOICE]
Table A: Prompt used for Sentiment Analysis task
Language Prompt in Emotion Classification Task
Indonesian (ind)
[INPUT]\nApakah emosi dari teks diatas? [LABELS_CHOICE]
Apakah emosi dari teks ini?\n Teks: [INPUT]\n Emosi: [LABELS_CHOICE]
Teks: [INPUT]\n\nTolong prediksikan emosi dari teks diatas: [LABELS_CHOICE]
English (eng)
[INPUT]\nWhat would be the emotion of the text above? [LABELS_CHOICE]
What is the emotion of this text?\nText: [INPUT]\nEmotion: [LABELS_CHOICE]
Text: [INPUT]\n\nPlease classify the emotion of above text: [LABELS_CHOICE]
Table A: Prompt used for Emotion Classification task
Language Prompt in Abusive Detection Task
Indonesian (ind)
[INPUT]\nApakah teks diatas kasar? [LABELS_CHOICE]
Apakah teks berikut ini kasar?\n[INPUT]\nJawab dengan [OPTIONS]: [LABELS_CHOICE]
[INPUT]\nApakah menurutmu teks diatas itu [OPTIONS]? [LABELS_CHOICE]
English (eng)
[INPUT]\nIs the text abusive? [LABELS_CHOICE]
Is the following text abusive?\n[INPUT]\nAnswer with [OPTIONS]: [LABELS_CHOICE]
[INPUT]\nDo you think the text is [OPTIONS]? [LABELS_CHOICE]
Table A: Prompt used for Abusive Detection task
Language Prompt in Clickbait Detection Task
Indonesian (ind)
[INPUT]\nApakah judul diatas clickbait? [LABELS_CHOICE]
Apakah judul berikut ini clickbait?\n[INPUT]\nJawab dengan [OPTIONS]: [LABELS_CHOICE]
[INPUT]\nApakah menurutmu teks diatas itu [OPTIONS]? [LABELS_CHOICE]
English (eng)
[INPUT]\nIs the title clickbait? [LABELS_CHOICE]
Is the following title a clickbait?\n[INPUT]\nAnswer with [OPTIONS]: [LABELS_CHOICE]
[INPUT]\nDo you think the text is [OPTIONS]? [LABELS_CHOICE]
Table A: Prompt used for Clickbait Detection task
Language Prompt in Rating Review Regression Task
Indonesian (ind)
[INPUT]\nBerapa rating dari teks review tersebut, dari 1 sampai 5? [LABELS_CHOICE]
[INPUT]\nDari 1 sampai 5, berapa rating dari review diatas? [LABELS_CHOICE]
[INPUT]\nDari 1 sampai 5 bintang, bagaimana menurutmu rating dari review tersebut? [LABELS_CHOICE]
English (eng)
[INPUT]\nWhat is the rating of the review above, from 1 to 5? [LABELS_CHOICE]
[INPUT]\nFrom 1 to 5, what is the rating of the review above? [LABELS_CHOICE]
[INPUT]\nFrom 1 to 5 stars, how would you rate the previous review? [LABELS_CHOICE]
Table A: Prompt used for Rating Review Regression task
Language Prompt in Hate Speech Detection Task
Indonesian (ind)
[INPUT]\nApakah teks diatas hatespeech? [LABELS_CHOICE]
Apakah teks berikut ini hatespeech\n[INPUT]\nJawab dengan [OPTIONS]: [LABELS_CHOICE]
[INPUT]\nApakah menurutmu teks diatas itu [OPTIONS]? [LABELS_CHOICE]
English (eng)
[INPUT]\nDo you think the text is hatespeech? Answer: [LABELS_CHOICE]
Is the following text a hatespeech?\n[INPUT]\nAnswer with [OPTIONS]: [LABELS_CHOICE]
[INPUT]\nDo you think the text is [OPTIONS]? [LABELS_CHOICE]
Table A: Prompt used for Hate Speech Detection task
Language Prompt in Next Tweet Prediction Task
Indonesian (ind)
Diberikan dua tweet\nA: [INPUT_A]\nB: [INPUT_B]\n\nApakah tweet B adalah sambungan dari tweet A? [LABELS_CHOICE]
Apakah tweet "[INPUT_B]" adalah sambungan dari tweet "[INPUT_A]"? [LABELS_CHOICE]
Tweet pertama: [INPUT_A].\nApakah "[INPUT_B]" merupakan sambungan dari tweet pertama? [LABELS_CHOICE]
English (eng)
Given two tweets\nA: [INPUT_A]\nB: [INPUT_B]\n\nIs tweet B is a continuation of tweet A? [LABELS_CHOICE]
Is tweet "[INPUT_B]" a continuation of tweet "[INPUT_A]"? [LABELS_CHOICE]
First Tweet: [INPUT_A].\nWould "[INPUT_B]" a continuation of the first tweet? [LABELS_CHOICE]
Table A: Prompt used for Next Tweet Prediction task
Language Prompt in NLI Task
Indonesian (ind)
[INPUT_A]\nBerdasarkan kutipan sebelumnya, apakah benar bahwa "[INPUT_B]"? [OPTIONS]? [LABELS_CHOICE]
[INPUT_A]\n\nPertanyaan: Apakah kalimat tersebut mengimplikasikan bahwa "[INPUT_B]"? [OPTIONS]? [LABELS_CHOICE]
Diberikan [INPUT_A]. Apakah kalimat tersebut sesuai dengan [INPUT_B]? [OPTIONS]? [LABELS_CHOICE]
English (eng)
[INPUT_A]\nBased on the previous passage, is it true that "[INPUT_B]"? Yes, no, or maybe? [LABELS_CHOICE]
[INPUT_A]\n\nQuestion: Does this imply that "[INPUT_B]"? Yes, no, or maybe? [LABELS_CHOICE]
Given that [INPUT_A]. Does it follow that [INPUT_B]? Yes, no, or maybe? [LABELS_CHOICE]
Table A: Prompt used for Natural Language Inference task
Language Prompt in Summary Task
Indonesian (ind)
[INPUT]\n===\nTulis rangkuman dari teks diatas dalam bahasa Indonesia:
Artikel dalam bahasa Indonesia: [INPUT]\nRangkuman dalam bahasa Indonesia:
[SOURCE]\nBagaimana kamu merangkum teks diatas dalam bahasa Indonesia?
English (eng)
[INPUT]\n===\nWrite a summary of the text above in Indonesian:
Article in Indonesian: [INPUT]\nSummary in Indonesian:
[SOURCE]\nHow would you rephrase that briefly in Indonesian?
Table A: Prompt used for Summary task
Language Prompt in Translation Task
Indonesian (ind)
Terjemahkan teks berikut dari bahasa [SOURCE] ke bahasa [TARGET].\nTeks: [INPUT]\nTerjemahan:
[INPUT]\nTerjemahkan teks diatas dari bahasa [SOURCE] ke bahasa [TARGET].
Teks dalah bahasa [SOURCE]: [INPUT]\nBagaimana kamu menterjemahkan teks diatas dalam bahasa [TARGET]?
English (eng)
Translate the following text from [SOURCE] to [TARGET].\nText: [INPUT]\nTranslation:
[INPUT]\nTranslate the text above from [SOURCE] to [TARGET].
Text in [SOURCE]: [INPUT]\nHow would you translate that in [TARGET]?
Table A: Prompt used for Translation task
0 20 40 60 80
XLM-R XNLI
XLM-R IndoNLI
XGLM 2.9B (ind)
XGLM 2.9B (eng)
BloomZ 3B (ind)
BloomZ 3B (eng)
Acc
0 20 40 60 80
F1
Figure A: Zero-shot generalization to NLU tasks in NusaNLU. Box plots show summary statistics on accuracy (left) and F1 (right). Points are per-dataset scores from the average of performances using three different prompts.
Dataset Name Lang Task XLM-R XNLI XLM-R IndoNLI XGLM 2.9B (id) XGLM 2.9B (en) BLOOMZ 3B (id) BLOOMZ 3B (en)
acc f1 acc f1 acc f1 acc f1 acc f1 acc f1
code_mixed_jv_id_id ind SA 29.79 29.66 16.48 15.20 71.00 29.10 15.59 14.90 21.73 22.88 19.17 23.96 code_mixed_jv_id_jv jav SA 32.86 34.12 17.71 18.42 70.83 28.65 13.92 12.27 18.22 18.38 16.07 16.58 emot ind EMOT 49.77 47.95 33.64 29.14 30.68 20.82 29.39 19.18 45.61 38.66 43.18 35.29 emotcmt ind EMOT 43.81 41.48 28.87 25.24 32.25 25.88 26.12 19.00 45.48 33.38 56.41 44.68 emotion_id_opinion ind EMOT 50.83 49.37 12.98 11.65 29.25 21.90 31.46 30.48 48.15 45.79 49.90 49.98 id_abusive ind AD 49.01 35.11 42.91 32.97 40.38 19.72 77.24 34.08 77.54 33.11 55.87 28.92 id_abusive_news_comment ind AD 30.78 21.74 7.29 8.29 25.20 23.16 22.26 20.11 28.37 22.21 31.04 25.51 id_google_play_review ind RR 9.59 12.94 51.22 23.70 42.38 14.82 43.70 16.70 73.90 37.64 63.70 35.97 id_google_play_review_posneg ind SA 92.32 85.63 89.78 76.38 83.84 52.25 83.25 65.93 93.44 87.15 87.76 81.28 id_hatespeech ind HSD 77.70 75.31 59.47 59.39 40.21 34.32 36.19 32.76 62.60 40.44 64.56 42.23 id_hoax_news ind HD 48.40 46.51 45.60 45.54 37.87 30.23 39.33 34.64 53.07 38.20 53.07 44.96 id_hsd_nofaaulia ind HSD 72.53 51.40 67.03 51.94 30.04 28.18 30.77 29.30 63.74 41.40 76.19 51.75 id_short_answer_grading ind SAG 17.50 7.56 17.75 5.03 12.58 4.67 21.29 8.55 20.41 8.70 19.24 5.45 id_stance ind SD 10.68 8.12 24.04 16.04 48.96 26.64 63.20 25.82 31.55 14.41 58.46 29.33 imdb_jv jav SA 21.01 14.45 33.22 20.71 49.47 38.74 48.77 39.44 37.42 31.25 31.27 31.13 indo_law ind LG 53.64 53.51 53.90 53.40 52.58 34.52 52.59 34.46 52.64 43.29 47.38 35.67 indolem_ntp ind NTP 61.26 32.95 31.69 20.72 33.87 32.56 43.20 40.03 76.21 57.08 77.60 53.95 indolem_sentiment ind SA 70.82 69.91 55.49 55.48 69.83 56.86 59.05 55.10 81.80 77.19 82.33 79.15 indonli ind NLI 35.77 28.02 35.52 27.84 35.82 29.71 35.59 24.36 52.27 41.38 56.82 44.62 indotacos ind LG 5.13 3.81 16.15 9.01 4.93 3.87 4.51 1.51 12.00 7.52 5.47 2.92 jadi_ide jav DI 41.61 33.73 30.12 28.18 33.33 21.06 33.33 22.70 33.40 18.31 33.75 17.99 karonese_sentiment btx SA 41.30 39.82 27.50 16.96 34.20 17.04 37.07 21.63 35.30 25.70 40.70 21.44 nusax_senti_ace ace SA 53.50 44.74 39.50 22.25 38.42 19.52 44.92 33.71 60.92 46.59 60.92 46.17 nusax_senti_ban ban SA 54.50 44.20 45.50 31.61 39.33 21.23 50.17 37.30 60.08 45.75 61.42 46.50 nusax_senti_bjn bjn SA 61.50 53.97 47.00 33.34 38.33 18.97 38.67 26.98 49.67 36.42 52.42 39.56 nusax_senti_bug bug SA 44.00 40.43 37.50 18.58 39.58 22.15 49.33 35.79 65.58 50.09 67.33 51.14 nusax_senti_eng eng SA 71.75 61.48 55.75 43.33 38.08 18.95 38.25 24.91 46.67 33.78 49.33 37.04 nusax_senti_ind ind SA 70.50 59.28 59.25 46.83 54.75 41.21 58.25 43.79 73.17 55.51 73.33 55.70 nusax_senti_jav jav SA 64.75 55.11 54.25 41.69 45.00 29.42 61.42 46.05 73.25 55.54 73.75 55.93 nusax_senti_mad mad SA 60.25 51.24 44.00 29.23 40.67 22.70 52.58 39.47 66.08 50.50 66.00 50.11 nusax_senti_min min SA 62.00 53.36 49.00 36.17 38.50 19.46 47.67 35.69 58.00 44.17 60.50 46.09 nusax_senti_nij nij SA 54.75 47.52 42.00 26.59 38.67 20.25 52.42 39.39 63.17 48.18 65.00 49.39 nusax_senti_sun sun SA 63.25 53.50 49.50 37.25 38.67 20.12 43.50 31.58 57.17 43.36 59.75 45.34 nusax_senti_bbc bbc SA 46.50 37.67 39.75 23.11 39.25 20.85 49.83 37.25 57.50 43.77 58.58 44.50 sentiment_nathasa_review ind SA 25.41 21.71 14.00 12.12 20.33 12.44 16.67 8.12 29.93 24.23 30.76 27.69 smsa ind SA 80.20 64.69 71.40 53.24 55.33 37.28 69.33 50.92 79.87 58.38 80.00 58.50 su_emot sun SA 45.95 44.35 29.59 27.72 28.49 16.25 25.06 11.16 34.59 25.25 24.99 10.16 wrete ind ENT 44 30.56 4.00 3.85 47.33 29.97 59.33 31.59 11.67 9.98 30.67 23.46
Table A: Details of zero-shot generalization to NLU tasks in NusaNLU. EMOT denotes emotion classification, AD denotes abusive detection, RR denotes review rating, SA denotes sentiment analysis, HD denotes hoax detection, HSD denotes hate speech detection, SAG denotes short answer grading, SF denotes stance detection, LG denotes legal classification, NLI denotes natural language inference, NTP denotes next tweet prediction, and ENT denotes entailment.
0 2 4 6 8 10 12 14
XGLM 2.9B (ind)
XGLM 2.9B (eng)
BloomZ 3B (ind)
BloomZ 3B (eng)
SacreBLEU
0 5 10 15 20 25 30 35 40
ROUGEL
Figure A: Zero-shot generalization to NLG tasks in NusaNLG. Box plots show summary statistics on SacreBLEU (left) and ROUGE-L (right). Points are per-dataset scores from the average of performances using 3 different prompts.
Dataset Name Lang Task XGLM 2.9B (id) XGLM 2.9B (en) BLOOMZ 3B (id) BLOOMZ 3B (en)
SacreBLEU ROUGEL SacreBLEU ROUGEL SacreBLEU ROUGEL SacreBLEU ROUGEL
bible_en_id eng-ind MT 1.11 8.41 0.49 3.69 3.39 20.52 3.82 22.81 bible_jv_id jav-ind MT 0.18 5.13 0.10 1.16 0.47 7.23 0.43 8.02 bible_su_id sun-ind MT 0.18 4.95 0.05 1.20 0.44 7.11 0.64 7.81 id_panl_bppt eng-ind MT 2.58 10.56 0.48 2.77 6.36 26.43 7.81 28.98 indo_general_mt_en_id eng-ind MT 3.51 9.41 1.35 3.40 8.82 25.51 9.92 26.82 indo_religious_mt_en_id eng-ind MT 1.80 9.27 0.25 1.81 4.49 22.06 5.94 26.76 minangnlp_mt min-ind MT 2.12 7.14 2.25 7.19 5.15 17.81 7.34 26.68 news_en_id eng-ind MT 2.66 10.26 0.48 2.62 6.56 24.56 8.83 28.92 nusax_mt_ace_ind ace-ind MT 0.72 6.55 0.47 3.19 1.47 11.47 1.77 13.28 nusax_mt_ban_ind ban-ind MT 1.09 6.94 0.82 3.68 2.58 12.92 2.54 14.61 nusax_mt_bbc_ind bbc-ind MT 1.59 7.94 0.89 4.33 3.03 15.49 3.96 18.59 nusax_mt_bjn_ind bjn-ind MT 0.30 4.43 0.28 2.06 0.52 6.80 0.58 7.66 nusax_mt_bug_ind bug-ind MT 2.06 10.31 0.19 2.50 3.12 22.78 4.33 24.55 nusax_mt_eng_ind eng-ind MT 0.72 4.77 0.66 3.38 1.00 6.84 1.76 10.35 nusax_mt_ind_ace ind-ace MT 1.03 5.26 0.77 4.30 1.72 8.34 3.43 15.45 nusax_mt_jav_ind jav-ind MT 0.53 5.47 0.30 2.48 1.13 10.00 1.38 10.71 nusax_mt_mad_ind mad-ind MT 1.81 8.64 1.06 4.83 3.39 16.11 5.28 20.66 nusax_mt_min_ind min-ind MT 0.64 6.14 0.63 3.38 1.39 11.40 1.68 12.07 nusax_mt_nij_ind nij-ind MT 0.84 6.73 0.81 4.08 2.01 12.80 2.26 13.54 nusax_mt_sun_ind sun-ind MT 0.39 5.00 0.29 2.40 0.98 8.94 1.18 9.96 nusax_mt_ind_ban ind-ban MT 1.67 7.19 1.10 4.88 1.83 10.43 3.66 15.10 nusax_mt_ind_bbc ind-bbc MT 0.33 2.46 0.36 2.64 0.47 4.05 0.67 4.69 nusax_mt_ind_bjn ind-bjn MT 0.57 3.70 2.15 12.76 3.16 19.82 4.66 24.47 nusax_mt_ind_bug ind-buh MT 0.86 4.75 0.70 3.73 1.39 8.92 2.28 12.59 nusax_mt_ind_eng ind-buh MT 0.44 3.50 0.42 3.00 0.77 5.73 0.86 6.09 nusax_mt_ind_jav ind-jav MT 1.87 7.69 0.85 4.30 2.47 10.62 6.01 21.41 nusax_mt_ind_mad ind-mad MT 0.71 4.69 0.59 3.31 0.72 6.98 1.36 8.91 nusax_mt_ind_min ind-min MT 0.99 5.41 0.76 3.80 2.00 10.50 4.01 15.56 nusax_mt_ind_nij ind-nij MT 0.50 3.39 0.47 3.30 0.71 4.83 1.43 7.87 nusax_mt_ind_sun ind-sun MT 1.14 7.27 0.59 3.74 2.11 13.00 2.06 13.87 parallel_su_id sun-ind MT 0.91 6.70 0.89 3.84 3.88 13.71 4.68 14.95 ted_en_id eng-ind MT 2.68 10.99 0.49 2.44 7.94 28.55 8.90 31.34 ud_id_csui ind-eng MT 1.06 3.69 5.58 16.68 8.54 27.28 14.94 38.89 indosum_fold0 ind SUM 2.64 5.53 9.73 12.24 3.55 12.21 3.07 11.88 xl_sum ind SUM 0.35 4.05 0.33 3.95 2.66 12.68 3.30 14.31 liputan6 ind SUM 1.47 4.12 1.92 7.48 3.62 13.92 2.67 13.13
EM F1 EM F1 EM F1 EM F1
facqa ind QA 0.21 5.61 0.21 4.72 61.95∗ 81.97∗ 61.95∗ 82.02∗ idk_mrc ind QA 0.71 3.67 0.28 4.31 37.05∗ 45.51∗ 34.91∗ 43.57∗ tydiqa_ind ind QA 0.31 5.52 0.27 4.67 63.08∗ 77.28∗ 62.85∗ 77.09∗
Table A: Details of zero-shot generalization to NLG tasks in NusaNLG. MT denotes machine translation, SUM denotes summarization, and QA denotes question answering. * the test data might be (partially) seen during the language pre-training and prompt tuning phase.
Dataset Lang Multilingual Monolingual Multi-task Single-task Multi-task ind jav sun
indspeech_digit_cdsr ind 2.36 0.22 97.51 97.65 43.84 indspeech_news_lvcsr ind 13.04 3.31 84.88 81.39 1.41 indspeech_teldialog_lvcsr ind 1.59 0.37 81.16 77.65 0.45 indspeech_teldialog_svcsr ind 9.01 1.72 94.58 97.03 0.65 librivox_indonesia_ind ind 14.24 15.41 86.23 >100 16.20 librivox_indonesia_ace ace 40.85 95.14 >100 92.36 100 indspeech_newstra_ethnicsr_ban ban 12.21 >100 56 59.14 43.98 librivox_indonesia_ban ban 21.24 >100 78.04 >100 100 indspeech_newstra_ethnicsr_btk btk 18.98 >100 81.24 74.67 64.77 librivox_indonesia_bug bug 41.59 96.70 >100 >100 100 indspeech_news_ethnicsr_jv jav 13.37 >100 37.50 93.32 100 indspeech_newstra_ethnicsr_jav jav 13.30 >100 42.70 72.77 57.34 librivox_indonesia_jav jav 29.05 81.18 60.70 >100 100 librivox_indonesia_min min 16.94 46.19 68.10 98.57 100 indspeech_news_ethnicsr_su sun 20.54 84.08 84.17 50.17 100 indspeech_newstra_ethnicsr_sun sun 12.41 >100 65.40 47.38 49.03 librivox_indonesia_sun sun 8.85 60.69 60.12 23.70 100
Average 17.03 >100 75.98 82.37 63.39
Table A: Per task WER (lower is better) of the wav2vec 2.0-pt model on all 17 ASR tasks in NusaASR.
Dataset Lang Multilingual Monolingual Multi-task Single-task Multi-task ind jav sun
indspeech_digit_cdsr ind 0.38 0.18 92.86 97.46 0.22 indspeech_news_lvcsr ind 0.82 0.65 67.33 80.12 1.37 indspeech_teldialog_lvcsr ind 0.59 0.09 68.65 74.26 0.22 indspeech_teldialog_svcsr ind 0.41 0.33 92.92 94.89 0.46 librivox_indonesia_ind ind 7.32 8.11 72.57 >100 8.37 librivox_indonesia_ace ace 31.94 91.67 90.28 89.58 49.31 indspeech_newstra_ethnicsr_ban ban 22.95 >100 36.57 43.69 21.50 librivox_indonesia_ban ban 19.16 >100 68.69 >100 35.98 indspeech_newstra_ethnicsr_btk btk 35.99 >100 59.79 61.34 40.92 librivox_indonesia_bug bug 53.30 >100 >100 >100 90.09 indspeech_news_ethnicsr_jv jav 22.30 >100 17.90 96.73 27.13 indspeech_newstra_ethnicsr_jav jav 21.41 >100 22.10 50.14 25.10 librivox_indonesia_jav jav 38.93 96.49 41.70 >100 44.10 librivox_indonesia_min min 18.10 70.48 52.86 79.05 24.29 indspeech_news_ethnicsr_su sun 35.47 87.02 74.13 43.25 44.38 indspeech_newstra_ethnicsr_sun sun 19.39 >100 39.67 25.71 20.45 librivox_indonesia_sun sun 7.51 67.63 49.13 6.36 15.03
Average 19.76 >100 61.76 75.76 26.41
Table A: Per task WER (lower is better) of the wav2vec 2.0-ft modes on all 17 ASR tasks in NusaASR.
Dataset Lang Multilingual Monolingual Multi-task Single-task Multi-task ind jav sun
indspeech_digit_cdsr ind 0.27 0.30 43.61 79.08 3.94 indspeech_news_lvcsr ind 2.56 2.40 59.95 97.42 2.08 indspeech_teldialog_lvcsr ind 0.38 0.57 58.14 94.44 0.97 indspeech_teldialog_svcsr ind 50.85 50.91 72.47 87.69 100 librivox_indonesia_ind ind 87.43 17.32 88.15 99.70 10.13 librivox_indonesia_ace ace 97.92 99.31 98.61 >100 37.50 indspeech_newstra_ethnicsr_ban ban 51.89 51.81 57.85 95.56 70.66 librivox_indonesia_ban ban 88.32 >100 87.38 99.07 39.25 indspeech_newstra_ethnicsr_btk btk 63.85 56.52 82.16 98.39 >100 librivox_indonesia_bug bug 100 >100 99.06 >100 56.60 indspeech_news_ethnicsr_jv jav 58.24 96.31 30.82 >100 53.84 indspeech_newstra_ethnicsr_jav jav 47.55 49.45 42.77 95.99 73.60 librivox_indonesia_jav jav 85.71 >100 46.94 >100 51.84 librivox_indonesia_min min 80.95 82.38 78.57 90.95 36.67 indspeech_news_ethnicsr_su sun 64.18 97.28 78.37 68.91 67.02 indspeech_newstra_ethnicsr_sun sun 54.65 53.51 66.26 73.02 72.71 librivox_indonesia_sun sun 77.46 88.44 68.21 41.62 27.17
Average 59.54 62.83 68.20 89.98 49.44
Table A: Per task WER (lower is better) of the Whisper model on all 17 ASR tasks in NusaASR.
Dataset Task Modal Languages
Korpus Nusantara (Indrayana, 2016; Hasbiansyah et al., 2016; Ningtyas et al., 2018; Etsa et al., 2018; Darwis et al., 2019; Wahyuni et al., 2019; Sujaini, 2019, 2020; Gunawan et al., 2021) MT Text ind, jav, xdy, bug, sun, mad, bjn, bbc, hak, msa, min, nan
Karonese dataset (Karo et al., 2022) SA Text btx
Sundanese-Indonesian Parallel Corpus (Ardiyanti Suryani et al., 2022b)
MT Text sun, ind
PoSTagged Sundanese Monolingual Corpus (Ardiyanti Suryani et al., 2022a) POS Text sun
Code-mixed Sentiment JV-ID (Tho et al., 2021) SA Text ind
ID-HSD-Nofaaulia (Aulia and Budi, 2019) HSD Text ind
Indo Wiki Paralel Corpora (Trisedya and Inastra, 2014) MT Text ind, jav, sun, min
INDspeech_DIGIT_CDSR (Sakti et al., 2004) ASR Speech ind
INDspeech_NEWS_EthnicSR (Sani et al., 2012) ASR Speech sun, jav
INDspeech_NEWS_LVCSR (Sakti et al., 2008a, 2004, 2013) ASR Speech ind
INDspeech_NEWS_TTS (Sakti et al., 2008b, 2010, 2013) TTS Speech ind
INDspeech_NEWSTRA_EthnicSR (Sakti and Nakamura, 2013, 2014; Novitasari et al., 2020) ASR Speech sun, jav, btk, ban
INDspeech_TELDIALOG_LVCSR (Sakti et al., 2008a, 2004, 2013) ASR Speech ind
INDspeech_TELDIALOG_SVCSR (Sakti et al., 2004) ASR Speech ind
Table A: List of private datasets that have been made public through NusaCrowd initiative. MT denotes machine translation, SA denotes sentiment analysis, POS denotes POS tagging, HSD denotes hate speech detection, ASR denotes automatic speech recognition, and TTS denotes text-to-speech.
Project #Dataset Languages Modality Open/crowd Opening accessinitiative? to private data
Global Data Initiative
XTREME (Hu et al., 2020) 9 40 text ✗ ✗ XGLUE (Liang et al., 2020) 11 19 text ✗ ✗ GEM (Gehrmann et al., 2021) 11 18 text ✓ ✗ GEMv2 (Gehrmann et al., 2022) 40 51 text ✓ ✗
Other Regional Data Initiative
CLUE (Xu et al., 2020) 9 1 (zho) text ✗ ✗ KLUE (Park et al., 2021) 8 1 (kor) text ✗ ✗ ALUE (Seelawi et al., 2021) 9 1 (ara) text ✗ ✗ IndicGLUE (Kakwani et al., 2020) 14 12 Indian languages text ✗ ✗ IndicNLG (Kumar et al., 2022) 5 11 Indian languages text ✗ ✗ IndicXTREME (Doddapaneni et al., 2022) 103 18 Indian languages text ✗ ✗
Indonesian Data Initiative
IndoNLU (Wilie et al., 2020) 12 1 (ind) text ✗ ✗ IndoLEM (Koto et al., 2020b) 12 1 (ind) text ✗ ✗ IndoNLG (Cahyawijaya et al., 2021b) 10 3 (ind, sun, jav) text ✗ ✗ NusaCrowd 137 19 Indonesian languages text, speech, image ✓ ✓
Table A: Comparison of NusaCrowd with other similar initiatives.
Dataset Description License Year Language Annotation Quality
Data Volume
Data Unit
Split
AM2iCo (Liu et al., 2021b) We present AM2ICO (Adversarial and Multilingual Meaning in Context), a wide coverage cross-lingual and multilingual evaluation set; it aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts for 14 language pairs. CC-BY 4.0
2021 ind, eng Crawling & human annotation 3,098 examples 1589 train, 500 validation, 1000 test
Barasa Barasa: Indonesian SentiWordNet for sentiment analysis MIT 2015 ind Unknown 16 MB No dataset split
CASA (Ilmania et al., 2018) CASA is an aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected from multiple Indonesian online automobile platforms. The dataset covers six aspects of car quality, where each label represents a sentiment for a single aspect with three possible values: positive, negative, and neutral. CCBY-SA 4.0
2018 ind Crawling & human annotation 1,080 sentences 810 train, 90 validation, 180 test
CC100 (Conneau et al., 2020) CC100 comprises of monolingual data for 100+ languages and also includes data for romanized languages. This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository. Common Crawl’s license 2020 ind, sun, jav Machine generated / Crawling w/o curation 36,052 MB No dataset split
COCO Captions ID (Sinurat, 2019) COCO Captions contains over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions are be provided for each image. This is an Indonesian version of COCO translated using Google Translate. CC-BY 4.0
2019 ind Machine generated / Crawling w/o curation 123,287 sentences 113287 train, 5000 validation, 5000 test
Continued on the next page
Table A – continued from the previous page Dataset Description License Year Language Annotation
Quality Data Volume
Data Unit
Split
CORD (Park et al., 2019) In this paper, we introduce a novel dataset called CORD, which stands for a Consolidated Receipt Dataset for post-OCR parsing. To the best of our knowledge, this is the first publicly available dataset which includes both box-level text and parsing class annotations. The parsing class labels are provided in two-levels. The eight superclasses include store, payment, menu, subtotal, and total. The eight superclasses are subdivided into 54 subclasses, e.g., store has nine subclasses including name, address, telephone, and fax. Furthermore, it also provides line annotations for the serialization task which is a newly emerging problem as a combination of the two tasks. CC-BY 4.0
2019 ind Crawling & human annotation 1,000 receipts 800 train, 100 validation, 100 test
CVSS (Jia et al., 2022) We introduce CVSS, a massively multilingual-toEnglish speech-to-speech translation (S2ST) corpus, covering sentence-level parallel S2ST pairs from 21 languages into English. CVSS is derived from the Common Voice (Ardila et al., 2020) speech corpus and the CoVoST 2 (Wang et al., 2021b) speech-to-text translation (ST) corpus, by synthesizing the translation text from CoVoST 2 into speech using state-of-the-art TTS systems. Two versions of translation speech in English are provided: 1) CVSS-C: All the translation speech is in a single high-quality canonical voice; 2) CVSS-T: The translation speech is in voices transferred from the corresponding source speech. In addition, CVSS provides normalized translation text which matches the pronunciation in the translation speech. CC-BY 4.0
2022 ind Crawling & human annotation 6 hours 2.6 train, 1.8 validation, 1.9 test
Cendana (Moeljadi et al., 2019) Cendana is a linguistically annotated corpus that includes some grammatical analyses, such as parts-ofspeech, phrases, relations between entities, and meaning representations. Cendana is built using tools developed in the Deep Linguistic Processing with HPSG (DELPHIN) community. GNU General Public License, version 2 2019 ind Human generation & curation 552 sentences No dataset split
Continued on the next page
Table A – continued from the previous page Dataset Description License Year Language Annotation
Quality Data Volume
Data Unit
Split
CoVoST 2 (Wang et al., 2021) With the aim to foster research in massive multilingual ST and ST for low resource language pairs, we release CoVoST 2, a large-scale multilingual ST corpus covering translations from 21 languages into English and from English into 15 languages. This represents the largest open dataset available to date from total volume and language coverage perspective. CC0 2020 ind, eng Crawling & human annotation 3 hours 1 train, 1 validation, 1 test
Code-mixed Sentiment JV-ID (Tho et al., 2021) Dataset terdiri dari 3.963 kalimat code-mixing dalam bahasa Indonesia dan bahasa Jawa yang dikumpulkan dari twitter. Label dataset terdiri dari 3 kelas sentimen, yaitu: positif, negatif, dan netral. Label sentimen dikumpulkan dengan melakukan anotasi manual untuk setiap tweet. CC-BY 3.0
2021 ind Human generation & curation 977 tweets No dataset split
Cross-lingual Outlinebased Dialogue (COD) (Majewska et al., 2023) Cross-lingual Outline-based Dialogue dataset (termed COD) enables natural language understanding, dialogue state tracking, and end-to-end dialogue modelling and evaluation in 4 diverse languages: Arabic, Indonesian, Russian, and Kiswahili. The data covers multi domain instances, e.g., bank, travel, weather, movies, music. Unknown 2022 ind Machine generated w/ human curation 194 dialogues 0 train, 92 validation, 102 test
Customer Review (Natasha Skincare) (Nurlaila et al., 2017) This dataset is collected from tweet costumer review from Natasha Skincare. This dataset contain of label emotion (joy, sad, angry, fear, disgust, surprise, or no emotion). Unknown 2017 ind Crawling & human annotation 124,263 tweets 87120 train, 37143 validation, 37143 test
EmoT (IndoNLU Split) (Saputri et al., 2018) EmoT is an emotion classification dataset collected from the social media platform Twitter. The dataset consists of around 4000 Indonesian colloquial language tweets, covering five different emotion labels: anger, fear, appiness, love, and sadness. CCBY-SA 4.0
2018 ind Human generation & curation 4,403 sentence pairs 3521 train, 440 validation, 442 test
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EmotCMT (Yulianti et al., 2021) EmotCMT is a Indonesian-English code-switching data collected from Twitter for emotion classification task. Unknown 2021 ind, eng Crawling & Human annotation 582 sentences No dataset split
Emotion Indonesian Public Opinion (Riccosan et al., 2022) The dataset is formed from Indonesian tweet containing six emotion values, namely anger, fear, joy, love, sad, and neutral. The total data in this dataset is 7,080 and it is fully cleaned and fully annotated. Each label has a varied amount of data distribution, including 1,130 data for anger, 911 data for fear, 1,275 data for joy, 760 data for love, 1,003 data for sad, and 2,001 data for neutral. CC-BYNC-ND 4.0 2022 ind Human generation & curation 7,080 tweets No dataset split
FacQA (Purwarianti et al., 2007) The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article . Each row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be found inside the corresponding short passage. There are six categories of questions: date, location, name, organization, person, and quantitative. CCBY-SA 4.0
2007 ind Human generation & curation 3,117 documents 2495 train, 311 dev, 311 test
HoASA (IndoNLU Split) (Azhar et al., 2019) HoASA is an aspect-based sentiment analysis dataset consisting of hotel reviews collected from the hotel aggregator platform, AiryRooms CCBY-SA 4.0
2019 ind Crawling & human annotation 9,450 sentences 7,560 train, 1890 test
Human Instructions - Indonesian (wikihow) (Chocron and Pareti, 2018) Human Instructions - Indonesian (wikihow) is 39.246 Human Instructions in Indonesian Extracted from wikiHow. Step-by-step instructions in Indonesian extracted from wikiHow and decomposed into a formal graph representation in RDF. Instructions are represented in RDF following the PROHOW vocabulary and data model. For example, the category, steps, requirements and methods of each set of instructions have been extracted. This dataset has been produced as part of the The Web of Know-How project. CC-BYNC-SA 4.0 2017 ind Crawling & human annotation 39,246 documents No dataset split
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ID Abusive (Ibrohim and Budi, 2018) ID Abusive is a Twitter dataset for abusive language detection in Indonesian. Pre-defined abusive words are used as queries to collect the tweets. The dataset is labeled into 3 labels: not abusive language, abusive but not offensive, and offensive language by 20 volunteer annotators. CC-BYNC-SA 4.0 2018 ind Crawling & human annotation 2,016 tweets No dataset split
ID Abusive Online News Comment (Kiasati Desrul and Romadhony, 2019) The dataset consists of comments that are in some of the top news stories in 2019, obtained from several online news/forum, such as: kompas, kaskus, and detik. The labeling process is carried out by a total of 10 annotators and each comment is annotated by 3 annotators. Each comment was labeled with one of the following labels: not abusive, abusive but not offensive, abusive and offensive. Unknown 2019 ind Machine generated / Crawling w/o curation 3,184 comments No dataset split
ID Coreference Resolution (Suherik and Purwarianti, 2017) ID Coreference resolution is news dataset aimed for coreference resolution task. This dataset consists of 1030 manually labelled sentences derived from IDENTIC parallel corpus. Unknown 2017 ind Crawling & human annotation 1,030 sentences 759 train, 0 validation, 108 test
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ID Multilabel HS (Ibrohim and Budi, 2019) ID Multilabel HS consists of hate speech and abusive language Twitter dataset from several previous researches consisting of (Alfina et al., 2017, 2018), (Putri, 2018), and (Ibrohim and Budi, 2018), and new tweets queried using specific abusive words/phrases. Labels used in the dataset are: HS (hate speech label), Abusive (abusive language label), HS_Individual (hate speech targeted to an individual), HS_Group (hate speech targeted to a group), HS_Religion (hate speech related to religion/creed), HS_Race (hate speech related to race/ethnicity), HS_Physical (hate speech related to physical/disability), HS_Gender (hate speech related to gender/sexual orientation), HS_Physical (hate related to other invective/slander), HS_Weak (weak hate speech), HS_Moderate (moderate hate speech), HS_Strong (strong hate speech). CC-BYNC-SA 4.0 2019 ind Crawling & human annotation 13,169 tweets No dataset split
ID Quora Question Pairs Quora Question Pairs (QQP) dataset consists of over 400,000 question pairs, and each question pair is annotated with a binary value indicating whether the two questions are paraphrase of each other. This dataset is translated version of QQP to Indonesian Language. Custom20 2021 ind Machine generated / Crawling w/o curation 149,011 sentence pairs 134084 train, 14927 validation, 0 test
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20https://www.quora.com/about/tos
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ID Short Answer Grading (Haidir and Purwarianti, 2020) ID Short Answer Grading dataset is dataset of questions using Edukasystem platform. It used 4 exams consisting of Biology and Geography subject. Two exams are used for training data and 2 exams are used for testing data. Exam used for training data has 15 questions and exam for testing data has 3 questions. The dataset has 30 questions + 7605 short answers as training data and 6 questions + 1560 short answers as testing data. The number of respondents is 534 different respondents. Assessment was carried out by 2 experts for Biology subject and 5 experts for Geography subject. The assessment is carried out on a scale of 0 to 5. Unknown 2020 ind Human generation & curation 9,165 sentences 7605 train, 0 validation, 1560 test
ID-HSDNofaaulia (Aulia and Budi, 2019) There have been many studies on detecting hate speech in short documents like Twitter data. But to our knowledge, research on long documents is rare, we suppose that the difficulty is increasing due to the possibility of the message of the text may be hidden. In this research, we explore in detecting hate speech on Indonesian long documents using machine learning approach. We build a new Indonesian hate speech dataset from Facebook. Unknown 2022 ind Crawling & human annotation 906 documents 815 train, 0 validation, 91 test
ID-HSDRiomulia (Alfina et al., 2017a) ID-HSD-RioMulia composed of tweets about the Jakarta Governor Election 2017, whose selection of candidates potentially triggers hate speech in relation to race, religion, and gender. Each tweet is labelled as either containing hate speech or not by 30 human annotators. GNU General Public License v3.0
2017 ind Crawling & human annotation 713 tweets No dataset split
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IDKMRC (Putri and Oh, 2022) IDK-MRC is an Indonesian Machine Reading Comprehension dataset that covers answerable and unanswerable questions. Based on the combination of the existing answerable questions in TyDiQA, the new unanswerable question in IDK-MRC is generated using question generation model and human-written question. Each paragraph in the dataset have a set of answerable and unanswerable question with the corresponding answer. (Note: the paper for this dataset is still under review on EMNLP 2022 – the anonymity period ends on Oct 6, 2022) CCBY-SA 4.0
2022 ind Machine generated w/ human curation 10,940 paragraph, question, and answer pairs 9332 train, 764 validation, 844 test
IMDb Javanese (Wongso et al., 2021) Large Movie Review Dataset translated to Javanese. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. Unknown 2021 jav Machine generated / Crawling w/o curation 50,000 sentences 25000 train, 0 validation, 25000 test
INDspeech_ DIGIT_ CDSR (Sakti et al., 2004) INDspeech_DIGIT_CDSR is the first Indonesian speech dataset for connected digit speech recognition (CDSR). The data was developed by TELKOMRisTI (R&D Division, PT Telekomunikasi Indonesia) in collaboration with Advanced Telecommunication Research Institute International (ATR) Japan and Bandung Institute of Technology (ITB) under the AsiaPacific Telecommunity (APT) project in 2004 [Sakti et al., 2004]. Although it was originally developed for a telecommunication system for hearing and speakingimpaired people, it can be used for other applications, i.e., automatic call centers that recognize telephone numbers. CC-BYNC-SA 4.0 2004 ind Human generation & curation 12444 [214]
utterances [speakers] 8440 train, 0 validation, 4004 test
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INDspeech_ NEWSTRA_ EthnicSR (Sakti and Nakamura, 2013) INDspeech_NEWSTRA_EthnicSR is a collection of graphemically balanced and parallel speech corpora of four major Indonesian ethnic languages: Javanese, Sundanese, Balinese, and Bataks. It was developed in 2013 by the Nara Institute of Science and Technology (NAIST, Japan) [Sakti et al., 2013]. The data has been used to develop Indonesian ethnic speech recognition in supervised learning [Sakti et al., 2014] and semisupervised learning [Novitasari et al., 2020] based on the Machine Speech Chain framework [Tjandra et al., 2020]. CC-BYNC-SA 4.0 2013 ind, sun, jav, ban, btk Human generation & curation 13000 [40]
utterances [speakers] 9000 train, 0 validation, 4000 test
INDspeech_ NEWS_ EthnicSR (Sani et al., 2012) INDspeech_NEWS_EthnicSR is a collection of Indonesian ethnic speech corpora (Javanese and Sundanese) for Indonesian ethnic speech recognition. It was developed in 2012 by the Nara Institute of Science and Technology (NAIST, Japan) in collaboration with the Bandung Institute of Technology (ITB, Indonesia) [Sani et al., 2012]. Furthermore, as all speakers utter the same sentences, it can also be used for voice conversion tasks. CC-BYNC-SA 4.0 2012 sun, jav Human generation & curation 2300 [20]
utterances [speakers] 2000 train, 0 validation, 300 test
INDspeech_ NEWS_ LVCSR (Sakti et al., 2008a) INDspeech_NEWS_LVCSR is the first Indonesian speech dataset for large vocabulary continuous speech recognition (LVCSR) with more than 40 hours of speech and 400 speakers [Sakti et al., 2008]. R&D Division of PT Telekomunikasi Indonesia (TELKOMRisTI) developed the data in 2005-2006, in collaboration with Advanced Telecommunication Research Institute International (ATR) Japan, as the continuation of the Asia-Pacific Telecommunity (APT) project [Sakti et al., 2004]. It has also been successfully used for developing Indonesian LVCSR in the Asian speech translation advanced research (A-STAR) project [Sakti et al., 2013]. CC-BYNC-SA 4.0 2008 ind Human generation & curation 44000 [400]
utterances [speakers] 39600 train, 0 validation, 4400 test
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INDspeech_ NEWS_ TTS (Sakti et al., 2008b) INDspeech_NEWS_TTS is a speech dataset for developing an Indonesian text-to-speech synthesis system [Sakti et al., 2008, Sakti et al., 2010]. The data was developed by Advanced Telecommunication Research Institute International (ATR) Japan under the Asian speech translation advanced research (A-STAR) project [Sakti et al., 2013]. CC-BYNC-SA 4.0 2008 ind Human generation & curation 2,012 utterances 1972 train, 0 validation, 40 test
INDspeech_ TELDIALOG_ LVCSR (Sakti et al., 2008a) INDspeech_TELDIALOG_LVCSR is one of the first Indonesian speech datasets for large vocabulary continuous speech recognition (LVCSR) [Sakti et al., 2008]. R&D Division of PT Telekomunikasi Indonesia (TELKOMRisTI) developed the data in 2005- 2006, in collaboration with Advanced Telecommunication Research Institute International (ATR) Japan, as the continuation of the Asia-Pacific Telecommunity (APT) project [Sakti et al., 2004]. It has also been successfully used for developing Indonesian LVCSR in the Asian speech translation advanced research (ASTAR) project [Sakti et al., 2013]. CC-BYNC-SA 4.0 2008 ind Human generation & curation 40000 [400]
utterances [speakers] 36000 train, 0 validation, 4000 test
INDspeech_ TELDIALOG_ SVCSR (Sakti et al., 2004) INDspeech_TELDIALOG_SVCSR is the first Indonesian speech dataset for small vocabulary continuous speech recognition (SVCSR). The data was developed by TELKOMRisTI (R&D Division, PT Telekomunikasi Indonesia) in collaboration with Advanced Telecommunication Research Institute International (ATR) Japan and Bandung Institute of Technology (ITB) under the Asia-Pacific Telecommunity (APT) project in 2004 [Sakti et al., 2004]. Although it was originally developed for a telecommunication system for hearing and speaking impaired people, it can be used for other applications, i.e., automatic call centers. Furthermore, as all speakers utter the same sentences, it can also be used for voice conversion tasks. CC-BYNC-SA 4.0 2004 ind Human generation & curation 20000 [200]
utterances [speakers] 10000 train, 0 validation, 10000 test
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Identic (Larasati, 2012) (Larasati, 2012) IDENTIC is an Indonesian-English parallel corpus for research purposes. The corpus is a bilingual corpus paired with English. The aim of this work is to build and provide researchers a proper Indonesian-English textual dataset and also to promote research in this language pair. The corpus contains texts coming from different sources with different genres. Unknown 2012 eng, ind Crawling & human annotation 45,000 sentences No dataset split
Idn-taggedcorpusCSUI (Dinakaramani et al., 2014) Id-tagged-corpus-CSUI is a POS tagging dataset contains about 10,000 sentences, collected from the PAN Localization Project tagged with 23 POS tag classes. CCBY-SA 4.0
2014 ind Crawling & human annotation 10,000 sentences 8000 train, 1000 validation, 1029 test
InSet Lexicon (Koto and Rahmaningtyas, 2017) Sentiment analysis from Twitter Unknown 2017 ind Crawling & human annotation 2,630 sentences 2630 test
IndQNER IndQNER is a NER dataset created by manually annotating 8 chapters in the Indonesian translation of Quran text. The dataset consists of 2476 named entities from 18 categories. Each named entity is labeled using BIO (Beginning-Inside-Outside) tagging format. Unknown 2022 ind Human generation & curation 3,118 sentences 2494 train, 312 validation, 312 test
Indo4B (Wilie et al., 2020) Indo4B is an Indonesian pre-training corpus collected from multiple online sources, Indo4B consists of 3.6B tokens and over more than 250M sentences. Indo4B has been used to pre-trained a large pre-trained language model called IndoBERT and IndoBERT-lite. CC-BYNC-SA 4.0 2020 ind Machine generated / Crawling w/o curation 3.6B tokens No dataset split
Indo4B Plus (Cahyawijaya et al., 2021b) Indo4BPlus is an Indonesian pre-training corpus derived from Indo4B. Indo4BPlus covers three languages in Indonesia, i.e., Indonesian, Javanese, and Sundanese. Indo4BPlus consists of 4B tokens with over more than 300M documents. CC-BYNC-SA 4.0 2021 ind, sun, jav Machine generated / Crawling w/o curation 4B tokens No dataset split
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IndoAMR (Ilmy and Khodra, 2020) IndoAMR is annotated Indonesia AMR parser from Indonesian simple sentences. Unknown 2021 ind Crawling & human annotation 1,130 sentences 700 train, 0 validation, 300 test
IndoCollex (Wibowo et al., 2021) List of Colloquial word Transformation with its label. e.g.: makan -> mkn (shortening). Data is published on IndoCollex: A Testbed for Morphological Transformation of Indonesian Word Colloquialism Research Paper published on ACL-IJCNLP 2021. Useful for morphological research. MIT 2021 ind Human generation & curation 2,126 sentence pairs 1637 train, 182 validation, 193 test
IndoCoref (Artari et al., 2021) IndoCoref is a coreference resolution dataset collected from Wikipedia. IndoCoref consists of 201 passages from wikipedia with manually labelled coreference by five annotators. MIT 2021 ind Human generation & curation 201 documents No dataset split
IndoLEM NTP (Koto et al., 2020b) IndoLEM next tweet prediction is a next tweet prediction dataset collected from tweeter CC BYSA 3.0 2020 ind Crawling & human annotation 8,382 instances 5681 train, 811 validation, 1890 test
IndoLEM Sentiment Analysis (Koto et al., 2020b) IndoLEM Sentiment Analysis is a textual sentiment analysis dataset collected from twitter CC BYSA 3.0 2020 ind Crawling & human annotation 5,048 sentences 3638 train, 399 validation, 1011 test
IndoLEM Tweet Ordering (Koto et al., 2020b) IndoLEM tweet ordering is a text ordering dataset collected from tweeter CC BYSA 3.0 2020 ind Crawling & human annotation 7,608 instances 5327 train, 760 validation, 1521 test
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IndoNLG Bible EnId (Cahyawijaya et al., 2021b) Bible En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the bible. We also add a Bible dataset to the English Indonesian translation task. Specifically, we collect an Indonesian and an English language Bible and generate a verse-aligned parallel corpus for the English-Indonesian machine translation task. We split the dataset and use 75% as the training set, 10% as the validation set, and 15% as the test set. Each of the datasets is evaluated in both directions, i.e., English to Indonesian (En → Id) and Indonesian to English (Id → En) translations. CCBY-SA 4.0
2021 eng, ind Crawling & human annotation 31,078 sentences 23308 training, 3109 validation, 4661 test
IndoNLG Bible JvId (Cahyawijaya et al., 2021b) Bible Jv-Id is a machine translation dataset containing Indonesian-Javanese parallel sentences collected from the bible. Analogous to the En ↔ Id and Su ↔ Id datasets, we create a new dataset for Javanese and Indonesian translation generated from the verse-aligned Bible parallel corpus with the same split setting. In terms of size, both the Su ↔ Id and Jv ↔ Id datasets are much smaller compared to the En ↔ Id dataset, because there are Bible chapters for which translations are available for Indonesian, albeit not for the local languages. CCBY-SA 4.0
2021 jav, ind Crawling & human annotation 7,957 sentences 5967 train, 797 validation, 1193 test
IndoNLG Bible SuId (Cahyawijaya et al., 2021b) Bible Su-Id is a machine translation dataset containing Sundanese Indonesian parallel sentences collected from the bible. Analogous to the En ↔ Id and Su ↔ Id datasets, we create a new dataset for Javanese and Indonesian translation generated from the verse-aligned Bible parallel corpus with the same split setting. In terms of size, both the Su ↔ Id and Jv ↔ Id datasets are much smaller compared to the En ↔ Id dataset, because there are Bible chapters for which translations are available for Indonesian, albeit not for the local languages CCBY-SA 4.0
2021 sun, ind Crawling & human annotation 7,958 sentences 5968 train, 797 validation, 1193 test
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IndoNLI (Mahendra et al., 2021) IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian. IndoNLI is annotated by both crowd workers and experts. The expert-annotated data is used exclusively as a test set. It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning. CCBY-SA 4.0
2021 ind Human generation & curation 17,712 sentence pairs 10330 train, 2197 validation, 5183 test
IndoNLU NERGrit (Wilie et al., 2020) NER Grit dataset is a NER dataset taken from the Grit-ID repository, and the labels are spans in IOB chunking representation. The dataset consists of three kinds of named entity tags, PERSON (name of person), PLACE (name of location), and ORGANIZATION (name of organization). CCBY-SA 4.0
2020 ind Unknown 2,090 sentences 1672 train, 209 validation, 209 test
IndoPuisi Puisi is an Indonesian poetic form. The dataset contains 7223 Indonesian puisi (poem) with its title and author. The data was scraped online using BeautifulSoup. The title and author column was produced using regex. MIT 2020 ind Machine generated / Crawling w/o curation 7,223 documents No dataset split
IndoSum (Kurniawan and Louvan, 2018) The Indosum dataset was collected from news aggregators covering six topics: entertainment, inspiration, sport, showbiz, headline, and technology. Compared to Liputan6, the summary label of Indosum is less abstractive, with novel 1-gram and novel 4-gram rates of 3.1% and 20.3%, respectively (Koto et al., 2020a). CCBY-SA 4.0
2021 ind Crawling & human annotation 18,773 sentences 14083 train, 1880 validation, 2810 test
IndoTacos IndoTacos dataset is tax court verdict summary collected from perpajakan.ddtc,co.id. It contains 12k tax court summary with its verdict: mengabulkan seluruhnya, mengabulkan sebagian, menolak, mengabulkan, lain-lain. This legal document is spesific for Indonesia tax cases. CC-BYNC-SA 4.0 2021 ind Machine generated / Crawling w/o curation 12,291 documents No dataset split
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Indo_ MultiModal_ CC_ 12M (Changpinyo et al., 2021) Conceptual 12M (CC12M) is a dataset with 12 million image-text pairs specifically meant to be used for visionand-language pre-training. Its data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M (CC3M). Indo_MultiModal_CC_ 12M is the Indonesian language version. Custom21 2022 ind Machine generated / Crawling w/o curation 1 GB No dataset split
Indo_ MultiModal_ LAION (Schuhmann et al., 2021) Indo_MultiModal_LAION is a translated subset of the LAION-400M dataset with 70M image-text pairs specifically meant to be used for vision-language pretraining in Indonesian language. LAION-400M is a dataset with 400M English (image, text) pairs, filtered with OpenAI‘s CLIP by calculating the cosine similarity between the text and image embeddings and dropping those with a similarity below 0.3. The threshold of 0.3 had been determined through human evaluations and seemed to be a good heuristic for estimating semantic image-text-content matching. The image-text-pairs have been extracted from the Common Crawl web data dump and are from random web pages crawled between 2014 and 2021. More info for LAION-400M: https://laion.ai/blog/laion-400-opendataset/. CC-BY 4.0
2022 ind Machine generated / Crawling w/o curation 7 GB No dataset split
Indo_ MultiModal_ PMD_ ID (Singh et al., 2022) Introduced in the FLAVA paper, Public Multimodal Dataset (PMD) is a collection of publicly-available image-text pair datasets. PMD contains 70M imagetext pairs in total with 68M unique images. The dataset contains pairs from Conceptual Captions, Conceptual Captions 12M, WIT, Localized Narratives, RedCaps, COCO, SBU Captions, Visual Genome and a subset of YFCC100M dataset. Indo_MultiModal_PMD_ Indonesia is the Indonesian language version. CC-BY4.0 2022 ind Machine generated / Crawling w/o curation 15 GB 0 train, 0 validation, 0 test
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21https://github.com/google-research-datasets/conceptual-12m/blob/main/LICENSE
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Indonesian Clickbait (William and Sari, 2020) The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news publishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo, Tribunnews, and Wowkeren. This dataset is comprised of mainly two parts; (i) 46,119 raw article data, and (ii) 15,000 clickbait annotated sample headlines. Annotation was conducted with 3 annotator examining each headline. Judgment were based only on the headline. The majority then is considered as the ground truth. In the annotated sample, our annotation shows 6,290 clickbait and 8,710 nonclickbait. CC-BY 4.0
2020 ind Crawling & human annotation 15,000 headlines No dataset split
Indonesian Frog Storytelling Corpus (Moeljadi, 2012) Indonesian written and spoken storytelling corpus, based on the twenty-eight pictures. Unknown 2014 ind Unknown 0 documents No dataset split
Indonesian Google Play Review Indonesian Google Play Review, dataset scrapped from e-commerce app on Google Play for sentiment analysis. CC-BY 4.0
2022 ind Machine generated / Crawling w/o curation 10,041 sentences 7028 train, 3012 validation, 0 test
Indonesian Hoax News Detection (Pratiwi et al., 2017) Indonesian Hoax News Detection is a dataset for hoax news detection. 600 Data are retrieved in Indonesian language with 372 valid news and 228 fake news.All data are manually labelled. CC-BY 4.0
2018 ind Human generation & curation 600 documents No dataset split
Indonesian Poem Tweets
Indonesian Poem tweets is dataset crawled from Twitter. The purpose of this data is to create text generation model for short text and make sure they are all coherence and rhythmic CC-BY 4.0
2022 ind Machine generated / Crawling w/o curation 16,427 tweets No dataset split
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Indonesian Stance (Jannati et al., 2018) ID Stance is a collection of Kompasiana articles that match with a pre-defined list of Indonesian politician names. Each article possesses a stance towards a candidate entity and election event, determined by annotators. Since the task is framed as a binary classification task, articles with no stance (neutral) are excluded from the gold-standard set. CC-BYNC-SA 4.0 2018 ind Crawling & human annotation 337 documents No dataset split
Indonesian WSD (Mahendra et al., 2018) Indonesian WSD is a word sense dissambiguation dataset automatically collected using CrossLingual WSD (CLWSD) approach by utilizing WordNet and parallel corpus GIZA++. The monolingual WSD model is built from training data and it is used to assign the correct sense to any previously unseen word in a new context. The dataset covers 6 commonly ambiguous words, i.e., alam, atas, kayu, anggur, perdana, and dasar, with a total of 2416 sentences. Unknown 2018 ind Machine generated / Crawling w/o curation 2,416 sentences No dataset split
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Indonesian general domain MT En-Id (Guntara et al., 2020) For the general domain, both Tatoeba and TALPCo are manually curated, but their sentences (especially Tatoeba) are very short compared to Wikimatrix. Therefore, for these two datasets, we do a random split involving all datasets in the domain for validation and testing, each having 2000 unique pairs not present in the training set. For the general domain, we mix shorter sentences from TALPCo and the longer ones from Wikimatrix as our validation and test data. We observe that Tatoeba has similar types of high-quality sentences like TALPCo has, albeit shorter. Therefore we choose TALPCo to be in the validation and test sets instead, because longer sentences mean more difficult and meaningful evaluation. Tatoeba dataset contains short sentences. However, they contain highquality full-sentence pairs with precise translation and is widely used in previous work in other languages (Artetxe and Schwenk, 2019b). Due to its simplicity, we do not use Tatoeba as our test and validation sets. We find that the Wikipedia scraper for Wikimatrix is faulty in some cases, causing some noise coming from unfiltered markup tags. CCBY-SA 4.0
2020 eng, ind Human generation & curation 1,811,300 sentences 1729472 train, 2000 validation, 2000 test
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Indonesian religious domain MT En-Id (Guntara et al., 2020) Religious domain consists of religious manuscripts or articles. These articles are different from news as they are not in a formal, informative style. Instead, they are written to advocate and inspire religious values, often times citing biblical or quranic anecdotes. The Tanzil dataset is a Quran translation dataset which has a relatively-imbalanced sentence length between the two languages, evidenced in Table 2, where an average Indonesian sentence in this dataset is about 50% longer than an average English one. Furthermore, an average pair of sentences in this dataset would, on average, have one of them twice as long as the other. However, we still decide to include the dataset in the domain to avoid overfitting because the remaining datasets are all about Christianity. Another interesting property in the religion domain corpus is the localized names, for example, David to Daud, Mary to Maryam, Gabriel to Jibril, and more. In contrast, entity names are usually kept unchanged in other domains. We also find quite a handful of Indonesian translations of JW300 are missing the end sentence dot (.), even though the end sentence dot is present in their English counterpart. Lastly, we also find some inconsistency in the transliteration, for example praying is sometimes written as salat or shalat, or repentance as tobat or taubat. CCBY-SA 4.0
2020 eng, ind Human generation & curation 1,068,400 sentences 579544 train, 5000 validation, 4823 test
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JATI (Moeljadi, 2017) JATI is a treebank built from a subset of parsed dictionary definition sentences. The main data for this study comes from the fifth edition of Kamus Besar Bahasa Indonesia (KBBI) (Amalia, 2016), the official and the most comprehensive dictionary for the Indonesian language. The dictionary definition sentences are parsed using the Indonesian Resource Grammar (INDRA) (Moeljadi, Bond, and Song 2015), a computational grammar for Indonesian in the Head-Driven Phrase Structure Grammar (HPSG) framework (Sag, Wasow, and Bender, 2003). JATI will be employed to build an ontology, in which knowledge is extracted from the semantic representation in Minimal Recursion Semantics (MRS) (Copestake et al., 2005). Unknown 2017 ind Human generation & curation 1,253 sentences No dataset split
JV-ID ASR (Kjartansson et al., 2018) This dataset contains transcribed audio data for Javanese. The dataset consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The dataset has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Reykjavik University and Universitas Gadjah Mada in Indonesia. CCBY-SA 4.0
2018 jav Human generation & curation 185,076 utterances No dataset split
JV-ID TTS (Sodimana et al., 2018) This dataset contains high-quality transcribed audio data for Javanese. The dataset consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The dataset has been manually quality checked, but there might still be errors.This dataset was collected by Google in collaboration with Gadjah Mada University in Indonesia. CCBY-SA 4.0
2018 ind, jav Human generation & curation 5,800 sentences No dataset split
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JaDi-Ide (Hidayatullah et al., 2020) The dataset is collected from Twitter. We named the dataset as Javanese dialect identification (JaDi-Ide). The dialect is classified into Standard Javanese, Ngapak Javanese, and East Javanese dialects. Unknown 2020 jav Crawling & human annotation 16,000 tweets No dataset split
KEPS (Mahfuzh et al., 2019) KEPS is a keyphrase extraction dataset consists of text from Twitter discussing banking products and services and is written in the Indonesian language. A phrase containing important information is considered a keyphrase. Text may contain one or more keyphrases since important phrases can be located at different positions. The dataset follows the IOB chunking format, which represents the position of the keyphrase. CCBY-SA 4.0
2019 ind Crawling & human annotation 1,247 documents 1000 train, 247 test
KaWAT (Kurniawan, 2019) We introduced KaWAT (Kata Word Analogy Task), a new word analogy task dataset for Indonesian. We evaluated on it several existing pretrained Indonesian word embeddings and embeddings trained on Indonesian online news corpus. We also tested them on two downstream tasks and found that pretrained word embeddings helped either by reducing the training epochs or yielding significant performance gains. Apache 2.0
2019 ind Human generation & curation 34,000 sentence pairs No dataset split
Kamus Alay (Salsabila et al., 2018) We provide a lexicon for text normalization of Indonesian colloquial words. We gathered 3,592 unique colloquial words-also known as bahasa alay -and manually annotated them with the normalized form. We built this lexicon from Instagram comments provided by Septiandri & Wibisono (2017). Unknown 2018 ind Human generation & curation 3,592 tokens No dataset split
Karonese dataset (Karo et al., 2022) Karonese dataset consist karonese text and the label (positive, negaitive or neutra). karonese text comes from multi domain social media, such us facebook, twitter, Instagram and Youtube Unknown 2022 btx Crawling & human annotation 1,001 sentences 0 train, 0 validation, 0 test
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Kethu (Arwidarasti et al., 2019) Kethu is a constituency treebank derived from Universitas Indonesia Constituency Treebank (UI-CTB) corpus Kethu converts UI-CTB treebank format into the widely accepted Penn Treebank format by adjusting the bracketing format for compound words as well as the POS tagset according to the Penn Treebank format. In addition, word segmentation and POS tagging of a number of tokens are also revised from the original UI-CTB corpus. Unknown 2019 ind Machine generated w/ human curation 1,030 sentences No dataset split
KoPI-CC (Korpus Perayapan Indonesia)
KoPI-CC (Korpus Perayapan Indonesia)-CC is Indonesian Only Extract from Common Crawl snapshots ,each snapshots get extracted using ungoliant oscar tools and get extra filtering using deduplication technique (Exact Hash Dup and Minhash LSH) CC0 2022 ind Machine generated / Crawling w/o curation 106 GB No dataset split
KoPI-CC_ News
KoPI(Korpus Perayapan Indonesia)-CC_News is Indonesian Only Extract from CC NEWS Common Crawl from 2016-2022(july) ,each snapshots get extracted using warcio and filter using fasttext CC0 2022 ind Machine generated / Crawling w/o curation 4 GB No dataset split
KoPI-NLLB KopI(Korpus Perayapan Indonesia)NLLB, is Indonesian family language(aceh,bali,banjar,indonesia,jawa,minang,sunda) only extracted from NLLB Dataset each language set also filtered using some deduplicate technique such as exact hash(md5) dedup technique and minhash LSH neardup ODCBY
2022 ind, sun, jav, min, ban, bjn, ace Machine generated / Crawling w/o curation 18 GB No dataset split
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Korpus Nusantara (Sujaini, 2020) The dataset is a combination of multiple machine translation works from the author, Herry Sujaini, covering Indonesian to 25 local dialects in Indonesia. Since not all dialects have ISO639-3 standard coding, as agreed with Pak Herry , we decided to group the dataset into the closest language family, i.e., Javanese, Dayak, Buginese, Sundanese, Madurese, Banjar, Batak Toba, Hakka (Khek), Malay, Minangkabau, and Min Nan (Teochew). Unknown 2022 ind, sun, jav, min, mad, bbc, bug, msa, xdy, hak, nan Human generation & curation 68,856 sentence pairs No dataset split
LibriVoxIndonesia (Wirawan, 2022)
The LibriVox Indonesia dataset consists of MP3 audio and a corresponding text file we generated from the public domain audiobooks LibriVox. We collected only languages in Indonesia for this dataset. The original LibriVox audiobooks or sound files’ duration varies from a few minutes to a few hours. Each audio file in the speech dataset now lasts from a few seconds to a maximum of 20 seconds. CC0 2022 ind, sun, jav, min, bug, ban, ace Machine generated / Crawling w/o curation 7,815 utterances No dataset split
Liputan6 Summarization (Koto et al., 2020a) The Liputan6 dataset was crawled from an online Indonesian news portal, which covers a wide range of topics, such as politics, sport, technology, business, health, and entertainment. There are two different experimental settings for Liputan6: Canonical, which includes all the test samples, and Xtreme, which only includes test samples with more than 90% novel 4- grams in the summary label. CCBY-SA 4.0
2021 ind Crawling & human annotation 224,637 sentences 193883 train, canonical: 10972 validation, 10972 test), extreme: 4948 validation, 3862 test),
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Local ID Abusive (Putri et al., 2021) Local ID Abusive is dataset aimed to be used for abusive and hate speech detection available in Javanese and Sundanese. The Javanese and Sundanese were annotated manually by annotator from each region. The annotation process involved multiple-step processes. It was carried by two annotators for each language, after an initial step where the guidelines were discussed and refined to reach unanimous comprehension. The annotation process gives 3449 and 2207 tweets for Javanese and Sundanese dataset respectively with 100% agreement. Unknown 2021 sun, jav Crawling & human annotation 5,656 sentences No dataset split
MaRVL (Liu et al., 2021a) Multicultural Reasoning over Vision and Language (MaRVL) is a dataset based on an ImageNet-style hierarchy representative of many languages and cultures (Indonesian, Mandarin Chinese, Swahili, Tamil, and Turkish). The selection of both concepts and images is entirely driven by native speakers. Afterwards, we elicit statements from native speakers about pairs of images. The task consists in discriminating whether each grounded statement is true or false. The present file contains all the dataset images and annotations. CC-BY 4.0
2021 ind Crawling & human annotation 1,128 image, image, concept, caption No dataset split
MinangNLP MT (Koto and Koto, 2020) MinangNLP MT is a machine translation dataset generated from two sentiment analysis dataset by manual translation to Minangkabau language MIT 2020 ind, min Crawling & human annotation 5,000 sentences 11,571 train, 1600 validation, 3200 test
MultiLexNorm (van der Goot et al., 2021a) MultiLexNorm is multilingual benchmark dataset for lexical normalization task for 12 languages, including Indonesian-English (code-mixed). Lexical normalization is the task of transforming an utterance into its standard form, word by word, including both oneto-many (1-n) and many-to-one (n-1) replacements. ID-EN dataset actually originates from Barik et.al. (2019) work. However, there is preprocessing work upon the original dataset. CC-BYNC-SA 4.0 2021 ind, eng Crawling & human annotation 13,949 tokens 13950 train, 4810 validation, 4367 test
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Multilingual Open Relations (Faruqui and Kumar, 2015) This dataset provides the set of automatically extracted relations obtained using cross-lingual annotation projection method. The data covers 61 languages, including Indonesian. Relation extraction is the task of assigning a semantic relationship between a pair of arguments. For example, from the sentence Soekarno lahir di Jawa Timur, the relation <Soekarno, born_in, Jawa Timur> is expected to be extracted. Unknown 2015 ind Machine generated / Crawling w/o curation 1,876 relations No dataset split
NER UGM (IndoLEM split) (Fachri, 2014) NER UGM is a named entity recognition dataset collected by UGM. We use IndoLEM split for the dataset. CC BYSA 3.0 2014 ind Crawling & human annotation 2,343 sentences 1687 train, 187 validation, 469 test
NER UI (IndoLEM split) (Gultom and Wibowo, 2017) NER UI is a named entity recognition dataset collected by UI. We use IndoLEM split for the dataset. CC BYSA 3.0 2017 ind Human generation & curation 2,125 sentences 1530 train, 170 validation, 425 test
NERGrit NER Grit dataset is a NER dataset taken from the Grit-ID repository, and the labels are spans in IOB chunking representation. The dataset consists of three kinds of named entity tags, PERSON (name of person), PLACE (name of location), and ORGANIZATION (name of organization). custom 2020 ind Unknown 17,437 sentences 12518 train, 2521 validation, 2398 test
NERP (IndoNLU Split) (Hoesen and Purwarianti, 2018) NERP is a NER dataset which is collected from several Indonesian news websites, labelled with 5 entity classes: PER (name of person), LOC (name of location), IND (name of product or brand), EVT (name of the event), and FNB (name of food and beverage). CCBY-SA 4.0
2018 ind Human generation & curation 8,400 sentences 6720 train, 840 validation, 840 test
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NLLB Seed (NLLB Team et al., 2022) NLLB Seed is a set of professionally-translated sentences in the Wikipedia domain. Data for NLLB-Seed was sampled from Wikimedia’s List of articles every Wikipedia should have, a collection of topics in different fields of knowledge and human activity. NLLBSeed consists of around six thousand sentences in 39 languages. NLLB-Seed is meant to be used for training rather than model evaluation. Due to this difference, NLLB-Seed does not go through the human quality assurance process present in FLORES-200. CCBY-NC 4.0
2022 eng, ace, bjn, bug Crawling & human annotation 30,965 sentence pairs No dataset split
Netifier Netifier dataset is a collection of scraped posts on famous social media sites in Indonesia, such as Instagram, Twitter, and Kaskus aimed to do multi-label toxicity classification. The dataset consists of 7,773 texts. The author manually labelled 7k samples into 4 categories: pornography, hate speech, racism, and radicalism. CC-BYNC-SA 4.0 2018 ind Crawling & human annotation 7,773 tweets 6995 train, 0 validation, 778 test
News En-Id MT (Moeljadi and Aminullah, 2020) News En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from news translation dataset (Guntara et al., 2020). The news dataset (Guntara et al., 2020) is collected from multiple sources: Pan Asia Networking Localization (PANL), Bilingual BBC news articles, Berita Jakarta, and GlobalVoices CCBY-SA 4.0
2021 eng, ind Crawling & human annotation 44,325 sentences 38469 train, 1953 validation, 1954 test
NusaX MT (Winata et al., 2023) The first-ever parallel resource for 10 low-resource languages in Indonesia. CCBY-SA 4.0
2022 ind, ace, jav, sun, min, bug, bbc, ban, nij, mad, bjn, eng Human generation & curation 132,000 sentence pairs 500 train, 100 dev, 400 test
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NusaX Sentiment (Winata et al., 2023) The first-ever parallel resource for 10 low-resource languages in Indonesia. CCBY-SA 4.0
2022 ind, ace, jav, sun, min, bug, bbc, ban, nij, mad, bjn, eng Human generation & curation 12,000 sentences 500 train, 100 dev, 400 test
OJW (Moeljadi and Aminullah, 2020) OJW is abbreviation of Old Javanese Wordnet. Unknown 2020 kaw Unknown 5,038 tokens No dataset split
PANL BPPT (Riza and Hakim, 2009) Parallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and Application of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Computing Capacity in Asia). The dataset contains around 24K sentences divided in 4 difference topics (Economic, international, Science and Technology and Sport). Unknown 2009 eng, ind Crawling & human annotation 24,000 sentences No dataset split
POSP (IndoNLU Split) (Hoesen and Purwarianti, 2018) POSP is an Indonesian part-of-speech tagging (POS) dataset collected from Indonesian news websites. The dataset consists of around 8000 sentences with 26 POS tags following the Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention. CCBY-SA 4.0
2018 ind Human generation & curation 8,400 sentences 6720 train, 840 validation, 840 test
ParaCotta (Aji et al., 2021) ParaCotta is a synthetic parallel paraphrase corpus generated from monolingual data and a neural machine translation system. Multiple translations were generated using beam search, and then paraphrase pairs were selected based on the lexical difference determined by their sentence BLEU. Unknown 2021 ind, eng Machine generated / Crawling w/o curation 6,000,000 sentence pairs No dataset split
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Parallel: Indonesian - Lampung Nyo (Abidin and Ahmad, 2021) Parallel Indonesian - Lampung Nyo corpus is constructed from documents taken from the Lampung language book for elementary and junior high school levels in the province of Lampung. The document data that has been collected will then be manually typed to be made into a parallel corpus in Indonesian – Lampung dialect of nyo and mono corpus in Lampung dialect of nyo. There are 3000 parallel corpus sentences collected in Indonesian - Lampung dialect of nyo and 3000 mono corpus sentences in Indonesian - Lampung dialect of nyo. Unknown 2021 abl, ind Crawling & human annotation 3,000 sentences No dataset split
PoSTagged Sundanese Monolingual Corpus (Ardiyanti Suryani et al., 2022a) The dataset consists of 3616 Sundanese sentences collected from several Sundanese online magazine (Mangle, Dewan Dakwah Jabar, and Balebat). The dataset is mannualy annotated with Part of Speech label by several Sundanese Language faculty students from UPI Bandung. CC0 2015 sun Crawling & human annotation 3,616 sentences No dataset split
SQuAD ID (Muis and Purwarianti, 2020) Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. This version is translated version of SQuAD to Indonesian Language. Unknown 2020 ind Machine generated / Crawling w/o curation 124,631 documents 102657 train, 11407 validation, 10567 test
STIFIndonesia (Wibowo et al., 2020) STIF-Indonesia is an Indonesian text style transfer dataset collected from Twitter. The style transfer is done from informal style to a formal style. STIFIndonesia consists of 52.5k sentences with 2.5k of which is manually annotated. MIT 2020 ind Crawling & human annotation 52.5k sentences 1922 train, 214 validation, 363 test
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SU-ID ASR (Kjartansson et al., 2018) This dataset contains transcribed audio data for Sundanese. The dataset consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The dataset has been manually quality checked, but there might still be errors. This dataset was collected by Google in Indonesia. CCBY-SA 4.0
2018 sun Human generation & curation 220,000 sentences No dataset split
SU-ID TTS (Sodimana et al., 2018) This dataset contains high-quality transcribed audio data for Sundanese. The dataset consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The dataset has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Universitas Pendidikan Indonesia. CCBY-SA 4.0
2018 sun Human generation & curation 4,213 utterances No dataset split
Sampiran (Siallagan and Alfina, 2023) Sampiran is a dataset for pantun generation. It consists of 7.8K Indonesian pantun, collected from various sources (online). Pantun is a traditional Malay poem consisting of four lines: two lines of deliverance and two lines of message. This dataset filtered the gathered Pantun to follow the general rules of Pantun; four lines with ABAB rhyme and eight to twelve syllables per line. AGPL3.0
2023 ind Crawling & human annotation 7,879 sentences No dataset split
Semeval STS Indo SemEval is a series of international natural language processing (NLP) research workshops whose mission is to advance the current state of the art in semantic analysis and to help create high-quality annotated datasets in a range of increasingly challenging problems in natural language semantics. This is a translated version of SemEval Dataset from 2012-2016 for Semantic Textual Similarity Task to Indonesian language. Unknown 2019 ind, eng Machine generated / Crawling w/o curation 12,901 sentence pairs 10293 train, 0 validation, 2608 test
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Singgalang (Alfina et al., 2017b) Singgalang is an automatically tagged Indonesian NER dataset collected from Wikipedia articles which uses DBpedia as the reference of the entity type. The dataset used expanded DBpedia of MDEE_Gazetteer as the reference to label the token and contains 48,957 sentences. Unknown 2017 ind Machine generated / Crawling w/o curation 48,957 sentences No dataset split
SmSA (Purwarianti and Crisdayanti, 2019) SmSA is a sentence-level sentiment analysis dataset consisting of of comments and reviews in Indonesian obtained from multiple online platforms with 3 possible sentiments:positive, negative, and neutral. The text was crawled and then annotated by several Indonesian linguists to construct this dataset. CCBY-SA 4.0
2020 ind Crawling & human annotation 12,760 sentences 11000 train, 1260 validation, 500 test
Sundanese Twitter Dataset for Emotion (Putra et al., 2020) Sunda Emotion dataset gathered from Twitter API between January and March 2019 with 2518 tweets in total. The tweets filtered by using some hashtags which are represented Sun-danese emotion, for instance, #persib, #corona, #saredih, #nyakakak, #garoblog, #sangsara, #gumujeng, #bungah, #sararieun, #ceurik, and #hariwang. This dataset contains four distinctive emotions: anger, joy, fear, and sadness.Each tweet is annotated using related emotion. For data validation, we consulted a Sundanese language teacher for expert validation Unknown 2020 sun Crawling & human annotation 2,518 sentences No dataset split
SundaneseIndonesian Parallel Corpus (Ardiyanti Suryani et al., 2022b) The dataset consists of 3616 Sundanese sentences taken from a Sundanese online magazing (Mangle), Dewan Dakwah Jabar, and Balebat. The dataset is m ually translated to Indonesian language by several Sundanese Language faculty students from UPI Bandung. CC0 2015 ind, sun Human generation & curation 3,616 sentence pairs No dataset split
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TALPCo (Nomoto et al., 2018) The TUFS Asian Language Parallel Corpus (TALPCo) is an open parallel corpus consisting of Japanese sentences and their translations into Korean, Burmese (Myanmar; the official language of the Republic of the Union of Myanmar), Malay (the national language of Malaysia, Singapore and Brunei), Indonesian, Thai, Vietnamese and English CC-BY 4.0
2018 ind, eng, kor, jpn, vie, tha, msa Unknown 0 sentences No dataset split
TED En-Id MT (Qi et al., 2018) TED En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from TED translation dataset (Qi et al., 2018), collected from TED talk transcripts CCBY-SA 4.0
2021 eng, ind Crawling & human annotation 93,262 sentences 87406 train, 2677 validation, 3179 test
TICO19 (Anastasopoulos et al., 2020) TICO-19 (Translation Initiative for COVID-19) is sampled from a variety of public sources containing COVID-19 related content, representing different domains (e.g., news, wiki articles, and others). TICO-19 includes 30 documents (3071 sentences, 69.7k words) translated from English into 36 languages: Amharic, Arabic (Modern Standard), Bengali, Chinese (Simplified), Dari, Dinka, Farsi, French (European), Hausa, Hindi, Indonesian, Kanuri, Khmer (Central), Kinyarwanda, Kurdish Kurmanji, Kurdish Sorani, Lingala, Luganda, Malay, Marathi, Myanmar, Nepali, Nigerian Fulfulde, Nuer, Oromo, Pashto, Portuguese (Brazilian), Russian, Somali, Spanish (Latin American), Swahili, Congolese Swahili, Tagalog, Tamil, Tigrinya, Urdu, Zulu. CC0 2020 ind, eng, ara, spa, fra, hin, por, rus, zho Machine generated w/ human curation 21,497 sentence pairs 0 train, 6797 validation, 14700 test
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TITMLIDN (Lestari, 2006) TITML-IDN (Tokyo Institute of Technology Multilingual - Indonesian) is collected to build a pioneering Indonesian Large Vocabulary Continuous Speech Recognition (LVCSR) System. In order to build an LVCSR system, high accurate acoustic models and large-scale language models are essential. Since Indonesian speech corpus was not available yet, we tried to collect speech data from 20 Indonesian native speakers (11 males and 9 females) to construct a speech corpus for training the acoustic model based on Hidden Markov Models (HMMs). A text corpus which was collected by ILPS, Informatics Institute, University of Amsterdam, was used to build a 40K-vocabulary dictionary and a n-gram language model. For research purposes only. If you use this corpus, you have to cite (Lestari et al, 2006). 2006 ind Human generation & curation 6,679 utterances No dataset split
TUFS Indonesia Constituency Tree (Nomoto, 2022) TUFS Indonesia Constituency Tree is annotated dataset for Indonesian language constituency tree. CC-BY 4.0
2022 ind, msa Human generation & curation 1,385 sentences No dataset split
TermA (Fernando et al., 2019) The TermA span-extraction dataset is collected from the hotel aggregator platform, AiryRooms. The dataset consists of thousands of hotel reviews, which each contain a span label for aspect and sentiment words representing the opinion of the reviewer on the corresponding aspect. The labels use InsideOutside-Beginning (IOB) tagging representation with two kinds of tags, aspect and sentiment. CCBY-SA 4.0
2019 ind Human generation & curation 5,000 documents 3000 train, 1000 validation, 1000 test
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Toxicity200 (NLLB Team et al., 2022) Toxicity-200 is a wordlist to detect toxicity in 200 languages. It contains files that include frequent words and phrases generally considered toxic because they represent: 1) frequently used profanities; 2) frequently used insults and hate speech terms, or language used to bully, denigrate, or demean; 3) pornographic terms; and 4) terms for body parts associated with sexual activity. CCBY-NC 4.0
2022 ind, ace, bjn, bug, jav Human generation & curation 226 phrases No dataset split
TyDiQA Id (Cahyawijaya et al., 2021b) For the question answering task, we use the TyDiQA (Clark et al., 2020) dataset. This dataset is collected from Wikipedia articles with human-annotated question and answer pairs covering 11 languages. The question-answer pairs are collected for each language without using translation services. We use the Indonesian data from the secondary Gold passage task of the TyDiQA dataset. As the original dataset only provides training and validation sets, we randomly split off 15% of the training data and use it as the test set. CCBY-SA 4.0
2021 ind Human generation & curation 6,267 sentences 4847 train, 565 validation, 855 test
UD_ IndonesianCSUI (Alfina et al., 2020) The UD_Indonesian-CSUI is a dependency treebank in Indonesian in the CoNLL-U format. It was converted from a consituency treebank (Kethu) while Kethu was also converted from another consituency treebank (IDN treebank). Currently, this treebank, consist of 1030 sentences. MIT 2020 ind Machine generated w/ human curation 1,030 sentences 656 train, 0 validation, 374 test
UD_ IndonesianGSD (McDonald et al., 2013) UD_Indonesian-GSD is is an Indonesian-GSD treebank dataset originally converted from the content head version of the universal dependency treebank v2.0 (legacy) in 2015. In order to comply with the latest Indonesian annotation guidelines, the treebank has undergone a major revision between UD releases v2.8 and v2.9 (2021). CC BYSA 3.0 2013 ind Human generation & curation 5,593 sentences 4477 train, 559 validation, 557 test
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UD_ IndonesianPUD (Alfina et al., 2019) An Indonesian dependency treebank that is part of a collection of 18 Parallel Universal Dependencies (PUD) treebanks. CC BYSA 3.0 2019 ind Human generation & curation 1,000 sentences 10 fold cross validation according to UD standard
UKARA 1.0 Challenge (Septiandri and Winatmoko, 2020) Ukara 1.0 Challenge dataset is a dataset for automatic short answer scoring system which is a collaboration project between FMIPA UGM and PUSPENDIK, Ministry of Education and Culture of Indonesia. It was intended to build supervised machine learning approach which is able to assign a score to student’s answer. The student’s answer usually consists of maximum 2-3 sentences. Unknown 2020 ind Human generation & curation 2,861 sentences 268 train, 215 validation, 855 test
Unimorph ID (Pimentel et al., 2021) The Universal Morphology (UniMorph) project is a collaborative effort to improve how NLP handles complex morphology in the world’s languages. The goal of UniMorph is to annotate morphological data in a universal schema that allows an inflected word from any language to be defined by its lexical meaning, typically carried by the lemma, and by a rendering of its inflectional form in terms of a bundle of morphological features from our schema Creative Commons AttributionShareAlike 3.0 Unported (CC BY-SA 3.0) 2021 ind Crawling & human annotation 27,714 forms 70% train, 10% validation, 20% test
VoxLingua107 (Valk and Alumäe, 2021) VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some postprocessing steps to filter out false positives. VoxLingua107 contains data for 107 languages, including Indonesian, Javanese, and Sundanese. CC-BY 4.0
2021 ind, sun, jav Crawling & human annotation 157 hours No dataset split
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WReTe (Setya and Mahendra, 2023) WReTe is an entailment dataset which consists of 450 sentence pairs constructed from Wikipedia revision history. The dataset contains pairs of sentences and binary semantic relations between the pairs. The data are labeled as entailed when the meaning of the second sentence can be derived from the first one, and not entailed otherwise. CCBY-SA 4.0
2018 ind Crawling & human annotation 450 sentence pairs 300 train, 50 validation, 100 test
WikiAnn (Pan et al., 2017) We developed a simple yet effective framework that can extract names from 282 languages and link them to an English KB. This framework follows a fully automatic training and testing pipeline, without the needs of any manual annotations or knowledge from native speakers. We release the following resources for each of these 282 languages: “silver-standard” name tagging and linking annotations with multiple levels of granularity, morphology analyzer if it’s a morphologically-rich language, and an end to-end name tagging and linking system. Attribution License (ODCBy) 2017 ind, eng, sun, jav, min, bug, bjn, tpi, ace, tdt, msa, jav-bms Machine generated / Crawling w/o curation 254,240 Number of name mentions No dataset split
WikiLingua (Ladhak et al., 2020) We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of crosslingual abstractive summarization systems. We extract article and summary pairs in 18 languages from WikiHow12, a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard article summary alignments across languages by aligning the images that are used to describe each how-to step in an article. CC-BYNC-SA 3.0 2020 ind, eng Crawling & human annotation 47,511 articlesummary pairs No dataset split
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XFACT (Gupta and Srikumar, 2021) The largest publicly available multilingual dataset for factual verification of naturally existing real world claims. The dataset contains short statements in 25 languages and is labeled for veracity by expert factcheckers. The dataset includes a multilingual evaluation benchmark that measures both out-of-domain generalization, and zero-shot capabilities of the multilingual models. MIT 2021 ind, eng Crawling & human annotation 3,548 evidenceslinksclaim 2231 train, 297 validation, 448 test
XCOPA (Ponti et al., 2020) Cross-lingual Choice of Plausible Alternatives (XCOPA), a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages, including Indonesian. The causal commonsense reasoning task consists two task variations, forward causal reasoning and backward reasoning. In forward causal reasoning, given the premise sentence, the model is asked to predict most reasonable result from two alternative. In backward causal reasoning, the model is asked to predict what causes the premise happens CC-BY 4.0
2021 ind Human generation & curation 600 sentences 0 train, 100 validation, 500 test
XLSum (Hasan et al., 2021) XL-Sum is a comprehensive and diverse dataset comprising 1 million professionally annotated articlesummary pairs from BBC, extracted using a set of carefully designed heuristics. The dataset covers 44 languages, including Indonesian. CC-BYNC-SA 4.0 2021 ind Crawling & human annotation 47,802 documentsummary pairs 38242 train, 4780 validation, 4780 test
XPersona Id (Lin et al., 2021) XPersona is a open-domain dialogue system on 7 languages including Indonesia. The test set is manually translated by exper annotators, while the training and validation set isareautomatically translated from the persona chat dataset with an additional manual keyword correction phase. CCBY-SA 4.0
2021 ind Machine generated w/ human curation 17,866 utterances 16878 train, 484 validation, 484 test
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Table A – continued from the previous page Dataset Description License Year Language Annotation
Quality Data Volume
Data Unit
Split
id-en-codemixed (Barik et al., 2019) This dataset contain 825 tweet instances of IndonesianEnglish, corresponding to four NLP tasks, i.e., tokenization, language identification, lexical normalization, and word translation. Data for lexical normalization task is curated in MultiLexNorm (already in Nusa Catalogue), but other tasks are not. Tokenization for social media data is not as trivial as splitting the token using white space delimiter. In this data, language identification is performed in token-level granularity. CC-BYNC-SA 4.0 2019 ind, eng Crawling & human annotation 22,736 tokens No dataset split
Indo Wiki Paralel Corpora (Trisedya and Inastra, 2014) Manually aligned parallel corpora from Wikipedia Unknown 2014 ind, sun, jav, min Crawling & human annotation 2,422 sentence pairs No dataset split
indo-law (Nuranti et al., 2022) This dataset consists of Indonesian court decision documents for general criminal cases that have been annotated for the document sections. The documents were taken from the website of the Indonesian Supreme Court Decision. There are 22,630 documents with xml format in this dataset, which each contains 11 tags that enclose the annotated sections of the court decision documents. Unknown 2022 ind Crawling & human annotation 22,630 documents No dataset split
xSID (van der Goot et al., 2021b) We introduce XSID, a new benchmark for crosslingual (X) Slot and Intent Detection in 13 languages from 6 language families, including a very lowresource dialect. CCBY-SA 4.0
2021 ind, eng Crawling & human annotation 5,370,460 sentences No dataset split
Table A: Overview of all datasets in NusaCrowd.

heading: Future Work
text: We encourage further collaboration and engagement with underrepresented language communities to ensure that their voices are heard and their needs are addressed in future NLP development. We remain committed to the principles of ethical research, diversity, inclusivity, and fairness, striving to promote social good through our work in the field of language technology