ymoslem/acl-abstracts huggingface dataset:
BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text (2021)

With the growing popularity of smart speakers, such as Amazon Alexa, speech is becoming one of the most important modes of human-computer interaction. Automatic speech recognition (ASR) is arguably the most critical component of such systems, as errors in speech recognition propagate to the downstream components and drastically degrade the user experience. A simple and effective way to improve the speech recognition accuracy is to apply automatic post-processor to the recognition result. However, training a post-processor requires parallel corpora created by human annotators, which are expensive and not scalable. To alleviate this problem, we propose Back TranScription (BTS), a denoising-based method that can create such corpora without human labor. Using a raw corpus, BTS corrupts the text using Text-to-Speech (TTS) and Speech-to-Text (STT) systems. Then, a post-processing model can be trained to reconstruct the original text given the corrupted input. Quantitative and qualitative evaluations show that a post-processor trained using our approach is highly effective in fixing non-trivial speech recognition errors such as mishandling foreign words. We present the generated parallel corpus and post-processing platform to make our results publicly available.
Zero-pronoun Data Augmentation for Japanese-to-English Translation (2021)

For Japanese-to-English translation, zero pronouns in Japanese pose a challenge, since the model needs to infer and produce the corresponding pronoun in the target side of the English sentence. However, although fully resolving zero pronouns often needs discourse context, in some cases, the local context within a sentence gives clues to the inference of the zero pronoun. In this study, we propose a data augmentation method that provides additional training signals for the translation model to learn correlations between local context and zero pronouns. We show that the proposed method significantly improves the accuracy of zero pronoun translation with machine translation experiments in the conversational domain.
Evaluation Scheme of Focal Translation for Japanese Partially Amended Statutes (2021)

For updating the translations of Japanese statutes based on their amendments, we need to consider the translation {\textquotedblleft}focality;{\textquotedblright} that is, we should only modify expressions that are relevant to the amendment and retain the others to avoid misconstruing its contents. In this paper, we introduce an evaluation metric and a corpus to improve focality evaluations. Our metric is called an Inclusive Score for DIfferential Translation: (ISDIT). ISDIT consists of two factors: (1) the n-gram recall of expressions unaffected by the amendment and (2) the n-gram precision of the output compared to the reference. This metric supersedes an existing one for focality by simultaneously calculating the translation quality of the changed expressions in addition to that of the unchanged expressions. We also newly compile a corpus for Japanese partially amendment translation that secures the focality of the post-amendment translations, while an existing evaluation corpus does not. With the metric and the corpus, we examine the performance of existing translation methods for Japanese partially amendment translations.
TMU NMT System with Japanese BART for the Patent task of WAT 2021 (2021)

In this paper, we introduce our TMU Neural Machine Translation (NMT) system submitted for the Patent task (Korean Japanese and English Japanese) of 8th Workshop on Asian Translation (Nakazawa et al., 2021). Recently, several studies proposed pre-trained encoder-decoder models using monolingual data. One of the pre-trained models, BART (Lewis et al., 2020), was shown to improve translation accuracy via fine-tuning with bilingual data. However, they experimented only Romanian!English translation using English BART. In this paper, we examine the effectiveness of Japanese BART using Japan Patent Office Corpus 2.0. Our experiments indicate that Japanese BART can also improve translation accuracy in both Korean Japanese and English Japanese translations.
System Description for Transperfect (2021)

In this paper, we describe our participation in the 2021 Workshop on Asian Translation (team ID: tpt{\_}wat). We submitted results for all six directions of the JPC2 patent task. As a first-time participant in the task, we attempted to identify a single configuration that provided the best overall results across all language pairs. All our submissions were created using single base transformer models, trained on only the task-specific data, using a consistent configuration of hyperparameters. In contrast to the uniformity of our methods, our results vary widely across the six language pairs.
Bering Lab`s Submissions on WAT 2021 Shared Task (2021)

This paper presents the Bering Lab`s submission to the shared tasks of the 8th Workshop on Asian Translation (WAT 2021) on JPC2 and NICT-SAP. We participated in all tasks on JPC2 and IT domain tasks on NICT-SAP. Our approach for all tasks mainly focused on building NMT systems in domain-specific corpora. We crawled patent document pairs for English-Japanese, Chinese-Japanese, and Korean-Japanese. After cleaning noisy data, we built parallel corpus by aligning those sentences with the sentence-level similarity scores. Also, for SAP test data, we collected the OPUS dataset including three IT domain corpora. We then trained transformer on the collected dataset. Our submission ranked 1st in eight out of fourteen tasks, achieving up to an improvement of 2.87 for JPC2 and 8.79 for NICT-SAP in BLEU score .
NLPHut`s Participation at WAT2021 (2021)

This paper provides the description of shared tasks to the WAT 2021 by our team {\textquotedblleft}NLPHut{\textquotedblright}. We have participated in the English{\textrightarrow}Hindi Multimodal translation task, English{\textrightarrow}Malayalam Multimodal translation task, and Indic Multi-lingual translation task. We have used the state-of-the-art Transformer model with language tags in different settings for the translation task and proposed a novel {\textquotedblleft}region-specific{\textquotedblright} caption generation approach using a combination of image CNN and LSTM for the Hindi and Malayalam image captioning. Our submission tops in English{\textrightarrow}Malayalam Multimodal translation task (text-only translation, and Malayalam caption), and ranks second-best in English{\textrightarrow}Hindi Multimodal translation task (text-only translation, and Hindi caption). Our submissions have also performed well in the Indic Multilingual translation tasks.
Improved English to Hindi Multimodal Neural Machine Translation (2021)

Machine translation performs automatic translation from one natural language to another. Neural machine translation attains a state-of-the-art approach in machine translation, but it requires adequate training data, which is a severe problem for low-resource language pairs translation. The concept of multimodal is introduced in neural machine translation (NMT) by merging textual features with visual features to improve low-resource pair translation. WAT2021 (Workshop on Asian Translation 2021) organizes a shared task of multimodal translation for English to Hindi. We have participated the same with team name CNLP-NITS-PP in two submissions: multimodal and text-only NMT. This work investigates phrase pairs injection via data augmentation approach and attains improvement over our previous work at WAT2020 on the same task in both text-only and multimodal NMT. We have achieved second rank on the challenge test set for English to Hindi multimodal translation where Bilingual Evaluation Understudy (BLEU) score of 39.28, Rank-based Intuitive Bilingual Evaluation Score (RIBES) 0.792097, and Adequacy-Fluency Metrics (AMFM) score 0.830230 respectively.
IITP at WAT 2021: System description for English-Hindi Multimodal Translation Task (2021)

Neural Machine Translation (NMT) is a predominant machine translation technology nowadays because of its end-to-end trainable flexibility. However, NMT still struggles to translate properly in low-resource settings specifically on distant language pairs. One way to overcome this is to use the information from other modalities if available. The idea is that despite differences in languages, both the source and target language speakers see the same thing and the visual representation of both the source and target is the same, which can positively assist the system. Multimodal information can help the NMT system to improve the translation by removing ambiguity on some phrases or words. We participate in the 8th Workshop on Asian Translation (WAT - 2021) for English-Hindi multimodal translation task and achieve 42.47 and 37.50 BLEU points for Evaluation and Challenge subset, respectively.
ViTA: Visual-Linguistic Translation by Aligning Object Tags (2021)

Multimodal Machine Translation (MMT) enriches the source text with visual information for translation. It has gained popularity in recent years, and several pipelines have been proposed in the same direction. Yet, the task lacks quality datasets to illustrate the contribution of visual modality in the translation systems. In this paper, we propose our system under the team name Volta for the Multimodal Translation Task of WAT 2021 from English to Hindi. We also participate in the textual-only subtask of the same language pair for which we use mBART, a pretrained multilingual sequence-to-sequence model. For multimodal translation, we propose to enhance the textual input by bringing the visual information to a textual domain by extracting object tags from the image. We also explore the robustness of our system by systematically degrading the source text. Finally, we achieve a BLEU score of 44.6 and 51.6 on the test set and challenge set of the multimodal task.
TMEKU System for the WAT2021 Multimodal Translation Task (2021)

We introduce our TMEKU system submitted to the English-Japanese Multimodal Translation Task for WAT 2021. We participated in the Flickr30kEnt-JP task and Ambiguous MSCOCO Multimodal task under the constrained condition using only the officially provided datasets. Our proposed system employs soft alignment of word-region for multimodal neural machine translation (MNMT). The experimental results evaluated on the BLEU metric provided by the WAT 2021 evaluation site show that the TMEKU system has achieved the best performance among all the participated systems. Further analysis of the case study demonstrates that leveraging word-region alignment between the textual and visual modalities is the key to performance enhancement in our TMEKU system, which leads to better visual information use.
Optimal Word Segmentation for Neural Machine Translation into Dravidian Languages (2021)

Dravidian languages, such as Kannada and Tamil, are notoriously difficult to translate by state-of-the-art neural models. This stems from the fact that these languages are morphologically very rich as well as being low-resourced. In this paper, we focus on subword segmentation and evaluate Linguistically Motivated Vocabulary Reduction (LMVR) against the more commonly used SentencePiece (SP) for the task of translating from English into four different Dravidian languages. Additionally we investigate the optimal subword vocabulary size for each language. We find that SP is the overall best choice for segmentation, and that larger dictionary sizes lead to higher translation quality.
Itihasa: A large-scale corpus for Sanskrit to English translation (2021)

This work introduces Itihasa, a large-scale translation dataset containing 93,000 pairs of Sanskrit shlokas and their English translations. The shlokas are extracted from two Indian epics viz., The Ramayana and The Mahabharata. We first describe the motivation behind the curation of such a dataset and follow up with empirical analysis to bring out its nuances. We then benchmark the performance of standard translation models on this corpus and show that even state-of-the-art transformer architectures perform poorly, emphasizing the complexity of the dataset.
NICT-5`s Submission To WAT 2021: MBART Pre-training And In-Domain Fine Tuning For Indic Languages (2021)

In this paper we describe our submission to the multilingual Indic language translation wtask {\textquotedblleft}MultiIndicMT{\textquotedblright} under the team name {\textquotedblleft}NICT-5{\textquotedblright}. This task involves translation from 10 Indic languages into English and vice-versa. The objective of the task was to explore the utility of multilingual approaches using a variety of in-domain and out-of-domain parallel and monolingual corpora. Given the recent success of multilingual NMT pre-training we decided to explore pre-training an MBART model on a large monolingual corpus collection covering all languages in this task followed by multilingual fine-tuning on small in-domain corpora. Firstly, we observed that a small amount of pre-training followed by fine-tuning on small bilingual corpora can yield large gains over when pre-training is not used. Furthermore, multilingual fine-tuning leads to further gains in translation quality which significantly outperforms a very strong multilingual baseline that does not rely on any pre-training.
How far can we get with one GPU in 100 hours? CoAStaL at MultiIndicMT Shared Task (2021)

This work shows that competitive translation results can be obtained in a constrained setting by incorporating the latest advances in memory and compute optimization. We train and evaluate large multilingual translation models using a single GPU for a maximum of 100 hours and get within 4-5 BLEU points of the top submission on the leaderboard. We also benchmark standard baselines on the PMI corpus and re-discover well-known shortcomings of translation systems and metrics.
IIIT Hyderabad Submission To WAT 2021: Efficient Multilingual NMT systems for Indian languages (2021)

This paper describes the work and the systems submitted by the IIIT-Hyderbad team in the WAT 2021 MultiIndicMT shared task. The task covers 10 major languages of the Indian subcontinent. For the scope of this task, we have built multilingual systems for 20 translation directions namely English-Indic (one-to- many) and Indic-English (many-to-one). Individually, Indian languages are resource poor which hampers translation quality but by leveraging multilingualism and abundant monolingual corpora, the translation quality can be substantially boosted. But the multilingual systems are highly complex in terms of time as well as computational resources. Therefore, we are training our systems by efficiently se- lecting data that will actually contribute to most of the learning process. Furthermore, we are also exploiting the language related- ness found in between Indian languages. All the comparisons were made using BLEU score and we found that our final multilingual sys- tem significantly outperforms the baselines by an average of 11.3 and 19.6 BLEU points for English-Indic (en-xx) and Indic-English (xx- en) directions, respectively.
Language Relatedness and Lexical Closeness can help Improve Multilingual NMT: IITBombay@MultiIndicNMT WAT2021 (2021)

Multilingual Neural Machine Translation has achieved remarkable performance by training a single translation model for multiple languages. This paper describes our submission (Team ID: CFILT-IITB) for the MultiIndicMT: An Indic Language Multilingual Task at WAT 2021. We train multilingual NMT systems by sharing encoder and decoder parameters with language embedding associated with each token in both encoder and decoder. Furthermore, we demonstrate the use of transliteration (script conversion) for Indic languages in reducing the lexical gap for training a multilingual NMT system. Further, we show improvement in performance by training a multilingual NMT system using languages of the same family, i.e., related languages.
Samsung R\&D Institute Poland submission to WAT 2021 Indic Language Multilingual Task (2021)

This paper describes the submission to the WAT 2021 Indic Language Multilingual Task by Samsung R{\&}D Institute Poland. The task covered translation between 10 Indic Languages (Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil and Telugu) and English. We combined a variety of techniques: transliteration, filtering, backtranslation, domain adaptation, knowledge-distillation and finally ensembling of NMT models. We applied an effective approach to low-resource training that consist of pretraining on backtranslations and tuning on parallel corpora. We experimented with two different domain-adaptation techniques which significantly improved translation quality when applied to monolingual corpora. We researched and applied a novel approach for finding the best hyperparameters for ensembling a number of translation models. All techniques combined gave significant improvement - up to +8 BLEU over baseline results. The quality of the models has been confirmed by the human evaluation where SRPOL models scored best for all 5 manually evaluated languages.
Multilingual Machine Translation Systems at WAT 2021: One-to-Many and Many-to-One Transformer based NMT (2021)

In this paper, we present the details of the systems that we have submitted for the WAT 2021 MultiIndicMT: An Indic Language Multilingual Task. We have submitted two separate multilingual NMT models: one for English to 10 Indic languages and another for 10 Indic languages to English. We discuss the implementation details of two separate multilingual NMT approaches, namely one-to-many and many-to-one, that makes use of a shared decoder and a shared encoder, respectively. From our experiments, we observe that the multilingual NMT systems outperforms the bilingual baseline MT systems for each of the language pairs under consideration.
IITP-MT at WAT2021: Indic-English Multilingual Neural Machine Translation using Romanized Vocabulary (2021)

This paper describes the systems submitted to WAT 2021 MultiIndicMT shared task by IITP-MT team. We submit two multilingual Neural Machine Translation (NMT) systems (Indic-to-English and English-to-Indic). We romanize all Indic data and create subword vocabulary which is shared between all Indic languages. We use back-translation approach to generate synthetic data which is appended to parallel corpus and used to train our models. The models are evaluated using BLEU, RIBES and AMFM scores with Indic-to-English model achieving 40.08 BLEU for Hindi-English pair and English-to-Indic model achieving 34.48 BLEU for English-Hindi pair. However, we observe that the shared romanized subword vocabulary is not helping English-to-Indic model at the time of generation, leading it to produce poor quality translations for Tamil, Telugu and Malayalam to English pairs with BLEU score of 8.51, 6.25 and 3.79 respectively.
ANVITA Machine Translation System for WAT 2021 MultiIndicMT Shared Task (2021)

This paper describes ANVITA-1.0 MT system, architected for submission to WAT2021 MultiIndicMT shared task by mcairt team, where the team participated in 20 translation directions: English{\textrightarrow}Indic and Indic{\textrightarrow}English; Indic set comprised of 10 Indian languages. ANVITA-1.0 MT system comprised of two multi-lingual NMT models one for the English{\textrightarrow}Indic directions and other for the Indic{\textrightarrow}English directions with shared encoder-decoder, catering 10 language pairs and twenty translation directions. The base models were built based on Transformer architecture and trained over MultiIndicMT WAT 2021 corpora and further employed back translation and transliteration for selective data augmentation, and model ensemble for better generalization. Additionally, MultiIndicMT WAT 2021 corpora was distilled using a series of filtering operations before putting up for training. ANVITA-1.0 achieved highest AM-FM score for English{\textrightarrow}Bengali, 2nd for English{\textrightarrow}Tamil and 3rd for English{\textrightarrow}Hindi, Bengali{\textrightarrow}English directions on official test set. In general, performance achieved by ANVITA for the Indic{\textrightarrow}English directions are relatively better than that of English{\textrightarrow}Indic directions for all the 10 language pairs when evaluated using BLEU and RIBES, although the same trend is not observed consistently when AM-FM based evaluation was carried out. As compared to BLEU, RIBES and AM-FM based scoring placed ANVITA relatively better among all the task participants.
ToxCCIn: Toxic Content Classification with Interpretability (2021)

Despite the recent successes of transformer-based models in terms of effectiveness on a variety of tasks, their decisions often remain opaque to humans. Explanations are particularly important for tasks like offensive language or toxicity detection on social media because a manual appeal process is often in place to dispute automatically flagged content. In this work, we propose a technique to improve the interpretability of these models, based on a simple and powerful assumption: a post is at least as toxic as its most toxic span. We incorporate this assumption into transformer models by scoring a post based on the maximum toxicity of its spans and augmenting the training process to identify correct spans. We find this approach effective and can produce explanations that exceed the quality of those provided by Logistic Regression analysis (often regarded as a highly-interpretable model), according to a human study.
Language that Captivates the Audience: Predicting Affective Ratings of TED Talks in a Multi-Label Classification Task (2021)

The aim of the paper is twofold: (1) to automatically predict the ratings assigned by viewers to 14 categories available for TED talks in a multi-label classification task and (2) to determine what types of features drive classification accuracy for each of the categories. The focus is on features of language usage from five groups pertaining to syntactic complexity, lexical richness, register-based n-gram measures, information-theoretic measures and LIWC-style measures. We show that a Recurrent Neural Network classifier trained exclusively on within-text distributions of such features can reach relatively high levels of overall accuracy (69{\%}) across the 14 categories. We find that features from two groups are strong predictors of the affective ratings across all categories and that there are distinct patterns of language usage for each rating category.
Partisanship and Fear are Associated with Resistance to COVID-19 Directives (2021)

Ideological differences have had a large impact on individual and community response to the COVID-19 pandemic in the United States. Early behavioral research during the pandemic showed that conservatives were less likely to adhere to health directives, which contradicts a body of work suggesting that conservative ideology emphasizes a rule abiding, loss aversion, and prevention focus. We reconcile this contradiction by analyzing semantic content of local press releases, federal press releases, and localized tweets during the first month of the government response to COVID-19 in the United States. Controlling for factors such as COVID-19 confirmed cases and deaths, local economic indicators, and more, we find that online expressions of fear in conservative areas lead to an increase in adherence to public health recommendations concerning COVID-19, and that expressions of fear in government press releases are a significant predictor of expressed fear on Twitter.