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Dataset Card for "indonlg"
Dataset Summary
IndoNLG is a collection of Natural Language Generation (NLG) resources for Indonesian, Javanese, and Sundanese with 6 kind of downstream tasks.
Supported Tasks and Leaderboards
Languages
- Indonesian, Javanese, Sundanese
Dataset Structure
We show detailed information for 10 datasets.
Data Instances
indosum
- Size of downloaded dataset files: 50.5 MB
- Size of the generated dataset: 50.5 MB
- Total amount of disk used: 50.5 MB
liputan6_canonical
(Liputan6 (Canonical))
- Size of downloaded dataset files: 363.6 MB
- Size of the generated dataset: 363.6 MB
- Total amount of disk used: 363.6 MB
liputan6_xtreme
(Liputan6 (Xtreme))
- Size of downloaded dataset files: 311.5 MB
- Size of the generated dataset: 311.5 MB
- Total amount of disk used: 311.5 MB
MT_ENGKJV_INZNTV
(Bible En↔Id)
- Size of downloaded dataset files: 10.2 MB
- Size of the generated dataset: 10.2 MB
- Total amount of disk used: 10.2 MB
MT_IMD_NEWS
(News En↔Id)
- Size of downloaded dataset files: 13.6 MB
- Size of the generated dataset: 13.6 MB
- Total amount of disk used: 13.6 MB
MT_JAVNRF_INZNTV
(Bible Jv↔Id)
- Size of downloaded dataset files: 3.3 MB
- Size of the generated dataset: 3.3 MB
- Total amount of disk used: 3.3 MB
MT_SUNIBS_INZNTV
(Bible Su↔Id)
- Size of downloaded dataset files: 2.6 MB
- Size of the generated dataset: 2.6 MB
- Total amount of disk used: 2.6 MB
MT_TED_MULTI
(TED En↔Id)
- Size of downloaded dataset files: 23.4 MB
- Size of the generated dataset: 23.4 MB
- Total amount of disk used: 23.4 MB
question_answering
- Size of downloaded dataset files: 4.7 MB
- Size of the generated dataset: 4.7 MB
- Total amount of disk used: 4.7 MB
xpersona
- Size of downloaded dataset files: 19.8 MB
- Size of the generated dataset: 19.8 MB
- Total amount of disk used: 19.8 MB
Data Fields
The data fields are the same among all splits
gem_id
: astring
feature.context
: astring
feature.input
: astring
feature.target
: astring
feature.references
: alist of string
feature.
Data Splits
task | train | validation | test |
---|---|---|---|
indosum | 14083 | 1880 | 2810 |
liputan6_canonical | 193,883 | 10,972 | 10,972 |
liputan6_xtreme | 193,883 | 4,948 | 3,862 |
MT_ENGKJV_INZNTV | 23,308 | 3,109 | 4,661 |
MT_IMD_NEWS | 38,469 | 1,953 | 1,954 |
MT_JAVNRF_INZNTV | 14083 | 1880 | 2810 |
MT_SUNIBS_INZNTV | 14083 | 1880 | 2810 |
MT_TED_MULTI | 14083 | 1880 | 2810 |
question_answering (TyDiQA) | 4,847 | 565 | 565 |
xpersona | 16,878 | 484 | 484 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@inproceedings{cahyawijaya-etal-2021-indonlg,
title = "{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation",
author = "Cahyawijaya, Samuel and
Winata, Genta Indra and
Wilie, Bryan and
Vincentio, Karissa and
Li, Xiaohong and
Kuncoro, Adhiguna and
Ruder, Sebastian and
Lim, Zhi Yuan and
Bahar, Syafri and
Khodra, Masayu and
Purwarianti, Ayu and
Fung, Pascale",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.699",
pages = "8875--8898",
abstract = "Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems. Unfortunately, the lack of publicly available NLG benchmarks for low-resource languages poses a challenging barrier for building NLG systems that work well for languages with limited amounts of data. Here we introduce IndoNLG, the first benchmark to measure natural language generation (NLG) progress in three low-resource{---}yet widely spoken{---}languages of Indonesia: Indonesian, Javanese, and Sundanese. Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important use case of NLG systems today. Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks. We collate a clean pretraining corpus of Indonesian, Sundanese, and Javanese datasets, Indo4B-Plus, which is used to pretrain our models: IndoBART and IndoGPT. We show that IndoBART and IndoGPT achieve competitive performance on all tasks{---}despite using only one-fifth the parameters of a larger multilingual model, mBART-large (Liu et al., 2020). This finding emphasizes the importance of pretraining on closely related, localized languages to achieve more efficient learning and faster inference at very low-resource languages like Javanese and Sundanese.",
}
Contributions
Thanks to @gentaiscool, @samuelcahyawijaya, and @bryanwilie for adding this dataset to GEM.