pwc-archive/papers-with-abstracts huggingface dataset:
Findings of the First Shared Task on Lifelong Learning Machine Translation
Authors: Loïc Barrault, Magdalena Biesialska, Marta R. Costa-jussà, Fethi Bougares, Olivier Galibert
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Lifelong learning, Machine Translation, Translation

A lifelong learning system can adapt to new data without forgetting previously acquired knowledge. In this paper, we introduce the first benchmark for lifelong learning machine translation. For this purpose, we provide training, lifelong and test data sets for two language pairs: English-German and English-French. Additionally, we report the results of our baseline systems, which we make available to the public. The goal of this shared task is to encourage research on the emerging topic of lifelong learning machine translation.
Findings of the WMT 2020 Shared Task on Chat Translation
Authors: M. Amin Farajian, António V. Lopes, André F. T. Martins, Sameen Maruf, Gholamreza Haffari
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: de-en, Translation

We report the results of the first edition of the WMT shared task on chat translation. The task consisted of translating bilingual conversational text, in particular customer support chats for the English-German language pair (English agent, German customer). This task varies from the other translation shared tasks, i.e. news and biomedical, mainly due to the fact that the conversations are bilingual, less planned, more informal, and often ungrammatical. Furthermore, such conversations are usually characterized by shorter and simpler sentences and contain more pronouns. We received 14 submissions from 6 participating teams, all of them covering both directions, i.e. En->De for agent utterances and De->En for customer messages. We used automatic metrics (BLEU and TER) for evaluating the translations of both agent and customer messages and human document-level direct assessments (DDA) to evaluate the agent translations.
Findings of the WMT 2020 Shared Task on Machine Translation Robustness
Authors: Lucia Specia, Zhenhao Li, Juan Pino, Vishrav Chaudhary, Francisco Guzmán, Graham Neubig, Nadir Durrani, Yonatan Belinkov, Philipp Koehn, Hassan Sajjad, Paul Michel, Xian Li
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Diversity, Machine Translation, Translation

We report the findings of the second edition of the shared task on improving robustness in Machine Translation (MT). The task aims to test current machine translation systems in their ability to handle challenges facing MT models to be deployed in the real world, including domain diversity and non-standard texts common in user generated content, especially in social media. We cover two language pairs – English-German and English-Japanese and provide test sets in zero-shot and few-shot variants. Participating systems are evaluated both automatically and manually, with an additional human evaluation for ”catastrophic errors”. We received 59 submissions by 11 participating teams from a variety of types of institutions.
The University of Edinburgh’s English-Tamil and English-Inuktitut Submissions to the WMT20 News Translation Task
Authors: Rachel Bawden, Alexandra Birch, Radina Dobreva, Arturo Oncevay, Antonio Valerio Miceli Barone, Philip Williams
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

We describe the University of Edinburgh’s submissions to the WMT20 news translation shared task for the low resource language pair English-Tamil and the mid-resource language pair English-Inuktitut. We use the neural machine translation transformer architecture for all submissions and explore a variety of techniques to improve translation quality to compensate for the lack of parallel training data. For the very low-resource English-Tamil, this involves exploring pretraining, using both language model objectives and translation using an unrelated high-resource language pair (German-English), and iterative backtranslation. For English-Inuktitut, we explore the use of multilingual systems, which, despite not being part of the primary submission, would have achieved the best results on the test set.
GTCOM Neural Machine Translation Systems for WMT20
Authors: Chao Bei, Hao Zong, Qingmin Liu, Conghu Yuan
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

This paper describes the Global Tone Communication Co., Ltd.’s submission of the WMT20 shared news translation task. We participate in four directions: English to (Khmer and Pashto) and (Khmer and Pashto) to English. Further, we get the best BLEU scores in the directions of English to Pashto, Pashto to English and Khmer to English (13.1, 23.1 and 25.5 respectively) among all the participants. Our submitted systems are unconstrained and focus on mBART (Multilingual Bidirectional and Auto-Regressive Transformers), back-translation and forward-translation. Also, we apply rules, language model and RoBERTa model to filter monolingual, parallel sentences and synthetic sentences. Besides, we validate the difference of the vocabulary built from monolingual data and parallel data.
DiDi’s Machine Translation System for WMT2020
Authors: Tanfang Chen, Weiwei Wang, Wenyang Wei, Xing Shi, Xiangang Li, Jieping Ye, Kevin Knight
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, Re-Ranking, Translation

This paper describes the DiDi AI Labs’ submission to the WMT2020 news translation shared task. We participate in the translation direction of Chinese->English. In this direction, we use the Transformer as our baseline model and integrate several techniques for model enhancement, including data filtering, data selection, back-translation, fine-tuning, model ensembling, and re-ranking. As a result, our submission achieves a BLEU score of 36.6 in Chinese->English.
Facebook AI’s WMT20 News Translation Task Submission
Authors: Peng-Jen Chen, Ann Lee, Changhan Wang, Naman Goyal, Angela Fan, Mary Williamson, Jiatao Gu
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Data Augmentation, Reranking, Translation

This paper describes Facebook AI’s submission to WMT20 shared news translation task. We focus on the low resource setting and participate in two language pairs, Tamil <-> English and Inuktitut <-> English, where there are limited out-of-domain bitext and monolingual data. We approach the low resource problem using two main strategies, leveraging all available data and adapting the system to the target news domain. We explore techniques that leverage bitext and monolingual data from all languages, such as self-supervised model pretraining, multilingual models, data augmentation, and reranking. To better adapt the translation system to the test domain, we explore dataset tagging and fine-tuning on in-domain data. We observe that different techniques provide varied improvements based on the available data of the language pair. Based on the finding, we integrate these techniques into one training pipeline. For En->Ta, we explore an unconstrained setup with additional Tamil bitext and monolingual data and show that further improvement can be obtained. On the test set, our best submitted systems achieve 21.5 and 13.7 BLEU for Ta->En and En->Ta respectively, and 27.9 and 13.0 for Iu->En and En->Iu respectively.
Linguistically Motivated Subwords for English-Tamil Translation: University of Groningen’s Submission to WMT-2020
Authors: Prajit Dhar, Arianna Bisazza, Gertjan van Noord
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, NMT, Translation

This paper describes our submission for the English-Tamil news translation task of WMT-2020. The various techniques and Neural Machine Translation (NMT) models used by our team are presented and discussed, including back-translation, fine-tuning and word dropout. Additionally, our experiments show that using a linguistically motivated subword segmentation technique (Ataman et al., 2017) does not consistently outperform the more widely used, non-linguistically motivated SentencePiece algorithm (Kudo and Richardson, 2018), despite the agglutinative nature of Tamil morphology.
The TALP-UPC System Description for WMT20 News Translation Task: Multilingual Adaptation for Low Resource MT
Authors: Carlos Escolano, Marta R. Costa-jussà, José A. R. Fonollosa
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Translation

In this article, we describe the TALP-UPC participation in the WMT20 news translation shared task for Tamil-English. Given the low amount of parallel training data, we resort to adapt the task to a multilingual system to benefit from the positive transfer from high resource languages. We use iterative backtranslation to fine-tune the system and benefit from the monolingual data available. In order to measure the effectivity of such methods, we compare our results to a bilingual baseline system.
An Iterative Knowledge Transfer NMT System for WMT20 News Translation Task
Authors: Jiwan Kim, Soyoon Park, Sangha Kim, Yoonjung Choi
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Decoder, NMT, Transfer Learning, Translation

This paper describes our submission to the WMT20 news translation shared task in English to Japanese direction. Our main approach is based on transferring knowledge of domain and linguistic characteristics by pre-training the encoder-decoder model with large amount of in-domain monolingual data through unsupervised and supervised prediction task. We then fine-tune the model with parallel data and in-domain synthetic data, generated with iterative back-translation. For additional gain, we generate final results with an ensemble model and re-rank them with averaged models and language models. Through these methods, we achieve +5.42 BLEU score compare to the baseline model.
Tohoku-AIP-NTT at WMT 2020 News Translation Task
Authors: Shun Kiyono, Takumi Ito, Ryuto Konno, Makoto Morishita, Jun Suzuki
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Reranking, Translation

In this paper, we describe the submission of Tohoku-AIP-NTT to the WMT’20 news translation task. We participated in this task in two language pairs and four language directions: English <–> German and English <–> Japanese. Our system consists of techniques such as back-translation and fine-tuning, which are already widely adopted in translation tasks. We attempted to develop new methods for both synthetic data filtering and reranking. However, the methods turned out to be ineffective, and they provided us with no significant improvement over the baseline. We analyze these negative results to provide insights for future studies.
NRC Systems for the 2020 Inuktitut-English News Translation Task
Authors: Rebecca Knowles, Darlene Stewart, Samuel Larkin, Patrick Littell
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, Translation

We describe the National Research Council of Canada (NRC) submissions for the 2020 Inuktitut-English shared task on news translation at the Fifth Conference on Machine Translation (WMT20). Our submissions consist of ensembled domain-specific finetuned transformer models, trained using the Nunavut Hansard and news data and, in the case of Inuktitut-English, backtranslated news and parliamentary data. In this work we explore challenges related to the relatively small amount of parallel data, morphological complexity, and domain shifts.
CUNI Submission for the Inuktitut Language in WMT News 2020
Authors: Tom Kocmi
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Transfer Learning, Translation

This paper describes CUNI submission to the WMT 2020 News Translation Shared Task for the low-resource scenario Inuktitut–English in both translation directions. Our system combines transfer learning from a Czech–English high-resource language pair and backtranslation. We notice surprising behaviour when using synthetic data, which can be possibly attributed to a narrow domain of training and test data. We are using the Transformer model in a constrained submission.
Samsung R&D Institute Poland submission to WMT20 News Translation Task
Authors: Mateusz Krubiński, Marcin Chochowski, Bartłomiej Boczek, Mikołaj Koszowski, Adam Dobrowolski, Marcin Szymański, Paweł Przybysz
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Re-Ranking, Translation

This paper describes the submission to the WMT20 shared news translation task by Samsung R&D Institute Poland. We submitted systems for six language directions: English to Czech, Czech to English, English to Polish, Polish to English, English to Inuktitut and Inuktitut to English. For each, we trained a single-direction model. However, directions including English, Polish and Czech were derived from a common multilingual base, which was later fine-tuned on each particular direction. For all the translation directions, we used a similar training regime, with iterative training corpora improvement through back-translation and model ensembling. For the En → Cs direction, we additionally leveraged document-level information by re-ranking the beam output with a separate model.
Speed-optimized, Compact Student Models that Distill Knowledge from a Larger Teacher Model: the UEDIN-CUNI Submission to the WMT 2020 News Translation Task
Authors: Ulrich Germann, Roman Grundkiewicz, Martin Popel, Radina Dobreva, Nikolay Bogoychev, Kenneth Heafield
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: CPU, GPU, Translation

We describe the joint submission of the University of Edinburgh and Charles University, Prague, to the Czech/English track in the WMT 2020 Shared Task on News Translation. Our fast and compact student models distill knowledge from a larger, slower teacher. They are designed to offer a good trade-off between translation quality and inference efficiency. On the WMT 2020 Czech ↔ English test sets, they achieve translation speeds of over 700 whitespace-delimited source words per second on a single CPU thread, thus making neural translation feasible on consumer hardware without a GPU.
The University of Edinburgh’s submission to the German-to-English and English-to-German Tracks in the WMT 2020 News Translation and Zero-shot Translation Robustness Tasks
Authors: Ulrich Germann
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Translation

This paper describes the University of Edinburgh’s submission of German <-> English systems to the WMT2020 Shared Tasks on News Translation and Zero-shot Robustness.
Contact Relatedness can help improve multilingual NMT: Microsoft STCI-MT @ WMT20
Authors: Vikrant Goyal, Anoop Kunchukuttan, Rahul Kejriwal, Siddharth Jain, Amit Bhagwat
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: NMT, Translation

We describe our submission for the English→Tamil and Tamil→English news translation shared task. In this submission, we focus on exploring if a low-resource language (Tamil) can benefit from a high-resource language (Hindi) with which it shares contact relatedness. We show utilizing contact relatedness via multilingual NMT can significantly improve translation quality for English-Tamil translation.
The AFRL WMT20 News Translation Systems
Authors: Jeremy Gwinnup, Tim Anderson
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, Translation

This report summarizes the Air Force Research Laboratory (AFRL) machine translation (MT) systems submitted to the news-translation task as part of the 2020 Conference on Machine Translation (WMT20) evaluation campaign. This year we largely repurpose strategies from previous years’ efforts with larger datasets and also train models with precomputed word alignments under various settings in an effort to improve translation quality.
SJTU-NICT’s Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task
Authors: Zuchao Li, Hai Zhao, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Collaborative Filtering, Language Modeling, Language Modelling, Machine Translation, NMT, Translation, Unsupervised Machine Translation

In this paper, we introduced our joint team SJTU-NICT ‘s participation in the WMT 2020 machine translation shared task. In this shared task, we participated in four translation directions of three language pairs: English-Chinese, English-Polish on supervised machine translation track, German-Upper Sorbian on low-resource and unsupervised machine translation tracks. Based on different conditions of language pairs, we have experimented with diverse neural machine translation (NMT) techniques: document-enhanced NMT, XLM pre-trained language model enhanced NMT, bidirectional translation as a pre-training, reference language based UNMT, data-dependent gaussian prior objective, and BT-BLEU collaborative filtering self-training. We also used the TF-IDF algorithm to filter the training set to obtain a domain more similar set with the test set for finetuning. In our submissions, the primary systems won the first place on English to Chinese, Polish to English, and German to Upper Sorbian translation directions.
Combination of Neural Machine Translation Systems at WMT20
Authors: Benjamin Marie, Raphael Rubino, Atsushi Fujita
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, NMT, Reranking, Translation

This paper presents neural machine translation systems and their combination built for the WMT20 English-Polish and Japanese->English translation tasks. We show that using a Transformer Big architecture, additional training data synthesized from monolingual data, and combining many NMT systems through n-best list reranking improve translation quality. However, while we observed such improvements on the validation data, we did not observed similar improvements on the test data. Our analysis reveals that the presence of translationese texts in the validation data led us to take decisions in building NMT systems that were not optimal to obtain the best results on the test data.
PROMT Systems for WMT 2020 Shared News Translation Task
Authors: Alexander Molchanov
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Translation

This paper describes the PROMT submissions for the WMT 2020 Shared News Translation Task. This year we participated in four language pairs and six directions: English-Russian, Russian-English, English-German, German-English, Polish-English and Czech-English. All our submissions are MarianNMT-based neural systems. We use more data compared to last year and update our back-translations with better models from the previous year. We show competitive results in terms of BLEU in most directions.
eTranslation’s Submissions to the WMT 2020 News Translation Task
Authors: Csaba Oravecz, Katina Bontcheva, László Tihanyi, David Kolovratnik, Bhavani Bhaskar, Adrien Lardilleux, Szymon Klocek, Andreas Eisele
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Translation

The paper describes the submissions of the eTranslation team to the WMT 2020 news translation shared task. Leveraging the experience from the team’s participation last year we developed systems for 5 language pairs with various strategies. Compared to last year, for some language pairs we dedicated a lot more resources to training, and tried to follow standard best practices to build competitive systems which can achieve good results in the rankings. By using deep and complex architectures we sacrificed direct re-usability of our systems in production environments but evaluation showed that this approach could result in better models that significantly outperform baseline architectures. We submitted two systems to the zero shot robustness task. These submissions are described briefly in this paper as well.
The ADAPT System Description for the WMT20 News Translation Task
Authors: Venkatesh Parthasarathy, Akshai Ramesh, Rejwanul Haque, Andy Way
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, NMT, Sentence, Translation

This paper describes the ADAPT Centre’s submissions to the WMT20 News translation shared task for English-to-Tamil and Tamil-to-English. We present our machine translation (MT) systems that were built using the state-of-the-art neural MT (NMT) model, Transformer. We applied various strategies in order to improve our baseline MT systems, e.g. onolin- gual sentence selection for creating synthetic training data, mining monolingual sentences for adapting our MT systems to the task, hyperparameters search for Transformer in lowresource scenarios. Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Tamil and Tamil-to-English translation tasks.
CUNI English-Czech and English-Polish Systems in WMT20: Robust Document-Level Training
Authors: Martin Popel
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: NMT, Sentence, Translation

We describe our two NMT systems submitted to the WMT 2020 shared task in English<->Czech and English<->Polish news translation. One system is sentence level, translating each sentence independently. The second system is document level, translating multiple sentences, trained on multi-sentence sequences up to 3000 characters long.
Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training
Authors: Christian Roest, Lukas Edman, Gosse Minnema, Kevin Kelly, Jennifer Spenader, Antonio Toral
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, NMT, Translation, Word Embeddings

Translating to and from low-resource polysynthetic languages present numerous challenges for NMT. We present the results of our systems for the English–Inuktitut language pair for the WMT 2020 translation tasks. We investigated the importance of correct morphological segmentation, whether or not adding data from a related language (Greenlandic) helps, and whether using contextual word embeddings improves translation. While each method showed some promise, the results are mixed.
OPPO’s Machine Translation Systems for WMT20
Authors: Tingxun Shi, Shiyu Zhao, Xiaopu Li, Xiaoxue Wang, Qian Zhang, Di Ai, Dawei Dang, Xue Zhengshan, Jie Hao
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, Translation

In this paper we demonstrate our (OPPO’s) machine translation systems for the WMT20 Shared Task on News Translation for all the 22 language pairs. We will give an overview of the common aspects across all the systems firstly, including two parts: the data preprocessing part will show how the data are preprocessed and filtered, and the system part will show our models architecture and the techniques we followed. Detailed information, such as training hyperparameters and the results generated by each technique will be depicted in the corresponding subsections. Our final submissions ranked top in 6 directions (English \leftrightarrow Czech, English \leftrightarrow Russian, French \rightarrow German and Tamil \rightarrow English), third in 2 directions (English \rightarrow German, English \rightarrow Japanese), and fourth in 2 directions (English \rightarrow Pashto and and English \rightarrow Tamil).