pwc-archive/papers-with-abstracts huggingface dataset:
HW-TSC’s Participation in the WMT 2020 News Translation Shared Task
Authors: Daimeng Wei, Hengchao Shang, Zhanglin Wu, Zhengzhe Yu, Liangyou Li, Jiaxin Guo, Minghan Wang, Hao Yang, Lizhi Lei, Ying Qin, Shiliang Sun
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Knowledge Distillation, Translation

This paper presents our work in the WMT 2020 News Translation Shared Task. We participate in 3 language pairs including Zh/En, Km/En, and Ps/En and in both directions under the constrained condition. We use the standard Transformer-Big model as the baseline and obtain the best performance via two variants with larger parameter sizes. We perform detailed pre-processing and filtering on the provided large-scale bilingual and monolingual dataset. Several commonly used strategies are used to train our models such as Back Translation, Ensemble Knowledge Distillation, etc. We also conduct experiment with similar language augmentation, which lead to positive results, although not used in our submission. Our submission obtains remarkable results in the final evaluation.
IIE’s Neural Machine Translation Systems for WMT20
Authors: Xiangpeng Wei, Ping Guo, Yunpeng Li, Xingsheng Zhang, Luxi Xing, Yue Hu
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Domain Adaptation, Knowledge Distillation, Machine Translation, Re-Ranking, Translation

In this paper we introduce the systems IIE submitted for the WMT20 shared task on German-French news translation. Our systems are based on the Transformer architecture with some effective improvements. Multiscale collaborative deep architecture, data selection, back translation, knowledge distillation, domain adaptation, model ensemble and re-ranking are employed and proven effective in our experiments. Our German-to-French system achieved 35.0 BLEU and ranked the second among all anonymous submissions, and our French-to-German system achieved 36.6 BLEU and ranked the fourth in all anonymous submissions.
Tencent Neural Machine Translation Systems for the WMT20 News Translation Task
Authors: Shuangzhi Wu, Xing Wang, Longyue Wang, Fangxu Liu, Jun Xie, Zhaopeng Tu, Shuming Shi, Mu Li
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Data Augmentation, Machine Translation, Translation

This paper describes Tencent Neural Machine Translation systems for the WMT 2020 news translation tasks. We participate in the shared news translation task on English \leftrightarrow Chinese and English \rightarrow German language pairs. Our systems are built on deep Transformer and several data augmentation methods. We propose a boosted in-domain finetuning method to improve single models. Ensemble is used to combine single models and we propose an iterative transductive ensemble method which can further improve the translation performance based on the ensemble results. We achieve a BLEU score of 36.8 and the highest chrF score of 0.648 on Chinese \rightarrow English task.
Russian-English Bidirectional Machine Translation System
Authors: Ariel Xv
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, Translation

This review depicts our submission to the WMT20 shared news translation task. WMT is the conference to assess the level of machine translation capabilities of organizations in the word. We participated in one language pair and two language directions, from Russian to English and from English to Russian. We used official training data, 102 million parallel corpora and 10 million monolingual corpora. Our baseline systems are Transformer models trained with the Sockeye sequence modeling toolkit, supplemented by bi-text data filtering schemes, back-translations, reordering and other related processing methods. The BLEU value of our translation result from Russian to English is 35.7, ranking 5th, while from English to Russian is 39.8, ranking 2th.
The DeepMind Chinese–English Document Translation System at WMT2020
Authors: Lei Yu, Laurent Sartran, Po-Sen Huang, Wojciech Stokowiec, Domenic Donato, Srivatsan Srinivasan, Alek Andreev, Wang Ling, Sona Mokra, Agustin Dal Lago, Yotam Doron, Susannah Young, Phil Blunsom, Chris Dyer
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Document Translation, Sentence, Sentence segmentation, Translation

This paper describes the DeepMind submission to the Chinese\rightarrowEnglish constrained data track of the WMT2020 Shared Task on News Translation. The submission employs a noisy channel factorization as the backbone of a document translation system. This approach allows the flexible combination of a number of independent component models which are further augmented with back-translation, distillation, fine-tuning with in-domain data, Monte-Carlo Tree Search decoding, and improved uncertainty estimation. In order to address persistent issues with the premature truncation of long sequences we included specialized length models and sentence segmentation techniques. Our final system provides a 9.9 BLEU points improvement over a baseline Transformer on our test set (newstest 2019).
The NiuTrans Machine Translation Systems for WMT20
Authors: Yuhao Zhang, Ziyang Wang, Runzhe Cao, Binghao Wei, Weiqiao Shan, Shuhan Zhou, Abudurexiti Reheman, Tao Zhou, Xin Zeng, Laohu Wang, Yongyu Mu, Jingnan Zhang, Xiaoqian Liu, Xuanjun Zhou, Yinqiao Li, Bei Li, Tong Xiao, Jingbo Zhu
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Knowledge Distillation, Machine Translation, Translation

This paper describes NiuTrans neural machine translation systems of the WMT20 news translation tasks. We participated in Japanese<->English, English->Chinese, Inuktitut->English and Tamil->English total five tasks and rank first in Japanese<->English both sides. We mainly utilized iterative back-translation, different depth and widen model architectures, iterative knowledge distillation and iterative fine-tuning. And we find that adequately widened and deepened the model simultaneously, the performance will significantly improve. Also, iterative fine-tuning strategy we implemented is effective during adapting domain. For Inuktitut->English and Tamil->English tasks, we built multilingual models separately and employed pretraining word embedding to obtain better performance.
The MUCOW word sense disambiguation test suite at WMT 2020
Authors: Yves Scherrer, Alessandro Raganato, Jörg Tiedemann
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: NMT, Translation, Word Sense Disambiguation

This paper reports on our participation with the MUCOW test suite at the WMT 2020 news translation task. We introduced MUCOW at WMT 2019 to measure the ability of MT systems to perform word sense disambiguation (WSD), i.e., to translate an ambiguous word with its correct sense. MUCOW is created automatically using existing resources, and the evaluation process is also entirely automated. We evaluate all participating systems of the language pairs English -> Czech, English -> German, and English -> Russian and compare the results with those obtained at WMT 2019. While current NMT systems are fairly good at handling ambiguous source words, we could not identify any substantial progress - at least to the extent that it is measurable by the MUCOW method - in that area over the last year.
WMT20 Document-Level Markable Error Exploration
Authors: Vilém Zouhar, Tereza Vojtěchová, Ondřej Bojar
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, Sentence, Translation

Even though sentence-centric metrics are used widely in machine translation evaluation, document-level performance is at least equally important for professional usage. In this paper, we bring attention to detailed document-level evaluation focused on markables (expressions bearing most of the document meaning) and the negative impact of various markable error phenomena on the translation. For an annotation experiment of two phases, we chose Czech and English documents translated by systems submitted to WMT20 News Translation Task. These documents are from the News, Audit and Lease domains. We show that the quality and also the kind of errors varies significantly among the domains. This systematic variance is in contrast to the automatic evaluation results. We inspect which specific markables are problematic for MT systems and conclude with an analysis of the effect of markable error types on the MT performance measured by humans and automatic evaluation tools.
Attention Transformer Model for Translation of Similar Languages
Authors: Farhan Dhanani, Muhammad Rafi
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Decoder, Machine Translation, Translation

This paper illustrates our approach to the shared task on similar language translation in the fifth conference on machine translation (WMT-20). Our motivation comes from the latest state of the art neural machine translation in which Transformers and Recurrent Attention models are effectively used. A typical sequence-sequence architecture consists of an encoder and a decoder Recurrent Neural Network (RNN). The encoder recursively processes a source sequence and reduces it into a fixed-length vector (context), and the decoder generates a target sequence, token by token, conditioned on the same context. In contrast, the advantage of transformers is to reduce the training time by offering a higher degree of parallelism at the cost of freedom for sequential order. With the introduction of Recurrent Attention, it allows the decoder to focus effectively on order of the source sequence at different decoding steps. In our approach, we have combined the recurrence based layered encoder-decoder model with the Transformer model. Our Attention Transformer model enjoys the benefits of both Recurrent Attention and Transformer to quickly learn the most probable sequence for decoding in the target language. The architecture is especially suited for similar languages (languages coming from the same family). We have submitted our system for both Indo-Aryan Language forward (Hindi to Marathi) and reverse (Marathi to Hindi) pair. Our system trains on the parallel corpus of the training dataset provided by the organizers and achieved an average BLEU point of 3.68 with 97.64 TER score for the Hindi-Marathi, along with 9.02 BLEU point and 88.6 TER score for Marathi-Hindi testing set.
Transformer-based Neural Machine Translation System for Hindi – Marathi: WMT20 Shared Task
Authors: Amit Kumar, Rupjyoti Baruah, Rajesh Kumar Mundotiya, Anil Kumar Singh
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, NMT, Translation

This paper reports the results for the Machine Translation (MT) system submitted by the NLPRL team for the Hindi – Marathi Similar Translation Task at WMT 2020. We apply the Transformer-based Neural Machine Translation (NMT) approach on both translation directions for this language pair. The trained model is evaluated on the corpus provided by shared task organizers, using BLEU, RIBES, and TER scores. There were a total of 23 systems submitted for Marathi to Hindi and 21 systems submitted for Hindi to Marathi in the shared task. Out of these, our submission ranked 6th and 9th, respectively.
Hindi-Marathi Cross Lingual Model
Authors: Sahinur Rahman Laskar, Abdullah Faiz Ur Rahman Khilji, Partha Pakray, Sivaji Bandyopadhyay
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, model, NMT, Translation

Machine Translation (MT) is a vital tool for aiding communication between linguistically separate groups of people. The neural machine translation (NMT) based approaches have gained widespread acceptance because of its outstanding performance. We have participated in WMT20 shared task of similar language translation on Hindi-Marathi pair. The main challenge of this task is by utilization of monolingual data and similarity features of similar language pair to overcome the limitation of available parallel data. In this work, we have implemented NMT based model that simultaneously learns bilingual embedding from both the source and target language pairs. Our model has achieved Hindi to Marathi bilingual evaluation understudy (BLEU) score of 11.59, rank-based intuitive bilingual evaluation score (RIBES) score of 57.76 and translation edit rate (TER) score of 79.07 and Marathi to Hindi BLEU score of 15.44, RIBES score of 61.13 and TER score of 75.96.
Transfer Learning for Related Languages: Submissions to the WMT20 Similar Language Translation Task
Authors: Lovish Madaan, Soumya Sharma, Parag Singla
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Transfer Learning, Translation

In this paper, we describe IIT Delhi’s submissions to the WMT 2020 task on Similar Language Translation for four language directions: Hindi <-> Marathi and Spanish <-> Portuguese. We try out three different model settings for the translation task and select our primary and contrastive submissions on the basis of performance of these three models. For our best submissions, we fine-tune the mBART model on the parallel data provided for the task. The pre-training is done using self-supervised objectives on a large amount of monolingual data for many languages. Overall, our models are ranked in the top four of all systems for the submitted language pairs, with first rank in Spanish -> Portuguese.
The IPN-CIC team system submission for the WMT 2020 similar language task
Authors: Luis A. Menéndez-Salazar, Grigori Sidorov, Marta R. Costa-jussà
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Domain Adaptation, Translation

This paper describes the participation of the NLP research team of the IPN Computer Research center in the WMT 2020 Similar Language Translation Task. We have submitted systems for the Spanish-Portuguese language pair (in both directions). The three submitted systems are based on the Transformer architecture and used fine tuning for domain Adaptation.
NMT based Similar Language Translation for Hindi - Marathi
Authors: Vandan Mujadia, Dipti Sharma
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, MORPH, NMT, POS, Translation

This paper describes the participation of team F1toF6 (LTRC, IIIT-Hyderabad) for the WMT 2020 task, similar language translation. We experimented with attention based recurrent neural network architecture (seq2seq) for this task. We explored the use of different linguistic features like POS and Morph along with back translation for Hindi-Marathi and Marathi-Hindi machine translation.
NUIG-Panlingua-KMI Hindi-Marathi MT Systems for Similar Language Translation Task @ WMT 2020
Authors: Atul Kr. Ojha, Priya Rani, Akanksha Bansal, Bharathi Raja Chakravarthi, Ritesh Kumar, John P. McCrae
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: NMT, Translation

NUIG-Panlingua-KMI submission to WMT 2020 seeks to push the state-of-the-art in Similar Language Translation Task for Hindi↔Marathi language pair. As part of these efforts, we conducteda series of experiments to address the challenges for translation between similar languages. Among the 4 MT systems prepared under this task, 1 PBSMT systems were prepared for Hindi↔Marathi each and 1 NMT systems were developed for Hindi↔Marathi using Byte PairEn-coding (BPE) into subwords. The results show that different architectures NMT could be an effective method for developing MT systems for closely related languages. Our Hindi-Marathi NMT system was ranked 8th among the 14 teams that participated and our Marathi-Hindi NMT system was ranked 8th among the 11 teams participated for the task.
Neural Machine Translation for Similar Languages: The Case of Indo-Aryan Languages
Authors: Santanu Pal, Marcos Zampieri
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, Translation

In this paper we present the WIPRO-RIT systems submitted to the Similar Language Translation shared task at WMT 2020. The second edition of this shared task featured parallel data from pairs/groups of similar languages from three different language families: Indo-Aryan languages (Hindi and Marathi), Romance languages (Catalan, Portuguese, and Spanish), and South Slavic Languages (Croatian, Serbian, and Slovene). We report the results obtained by our systems in translating from Hindi to Marathi and from Marathi to Hindi. WIPRO-RIT achieved competitive performance ranking 1st in Marathi to Hindi and 2nd in Hindi to Marathi translation among 22 systems.
Neural Machine Translation between similar South-Slavic languages
Authors: Maja Popović, Alberto Poncelas
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, NMT, Translation

This paper describes the ADAPT-DCU machine translation systems built for the WMT 2020 shared task on Similar Language Translation. We explored several set-ups for NMT for Croatian–Slovenian and Serbian–Slovenian language pairs in both translation directions. Our experiments focus on different amounts and types of training data: we first apply basic filtering on the OpenSubtitles training corpora, then we perform additional cleaning of remaining misaligned segments based on character n-gram matching. Finally, we make use of additional monolingual data by creating synthetic parallel data through back-translation. Automatic evaluation shows that multilingual systems with joint Serbian and Croatian data are better than bilingual, as well as that character-based cleaning leads to improved scores while using less data. The results also confirm once more that adding back-translated data further improves the performance, especially when the synthetic data is similar to the desired domain of the development and test set. This, however, might come at a price of prolonged training time, especially for multitarget systems.
Infosys Machine Translation System for WMT20 Similar Language Translation Task
Authors: Kamalkumar Rathinasamy, Amanpreet Singh, Balaguru Sivasambagupta, Prajna Prasad Neerchal, Vani Sivasankaran
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, Translation

This paper describes Infosys’s submission to the WMT20 Similar Language Translation shared task. We participated in Indo-Aryan language pair in the language direction Hindi to Marathi. Our baseline system is byte-pair encoding based transformer model trained with the Fairseq sequence modeling toolkit. Our final system is an ensemble of two transformer models, which ranked first in WMT20 evaluation. One model is designed to learn the nuances of translation of this low resource language pair by taking advantage of the fact that the source and target languages are same alphabet languages. The other model is the result of experimentation with the proportion of back-translated data to the parallel data to improve translation fluency.
Document Level NMT of Low-Resource Languages with Backtranslation
Authors: Sami Ul Haq, Sadaf Abdul Rauf, Arsalan Shaukat, Abdullah Saeed
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, NMT, Sentence, Translation

This paper describes our system submission to WMT20 shared task on similar language translation. We examined the use of documentlevel neural machine translation (NMT) systems for low-resource, similar language pair Marathi−Hindi. Our system is an extension of state-of-the-art Transformer architecture with hierarchical attention networks to incorporate contextual information. Since, NMT requires large amount of parallel data which is not available for this task, our approach is focused on utilizing monolingual data with back translation to train our models. Our experiments reveal that document-level NMT can be a reasonable alternative to sentence-level NMT for improving translation quality of low resourced languages even when used with synthetic data.
Multilingual Neural Machine Translation: Case-study for Catalan, Spanish and Portuguese Romance Languages
Authors: Pere Vergés Boncompte, Marta R. Costa-jussà
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Decoder, Machine Translation, Translation

In this paper, we describe the TALP-UPC participation in the WMT Similar Language Translation task between Catalan, Spanish, and Portuguese, all of them, Romance languages. We made use of different techniques to improve the translation between these languages. The multilingual shared encoder/decoder has been used for all of them. Additionally, we applied back-translation to take advantage of the monolingual data. Finally, we have applied fine-tuning to improve the in-domain data. Each of these techniques brings improvements over the previous one. In the official evaluation, our system was ranked 1st in the Portuguese-to-Spanish direction, 2nd in the opposite direction, and 3rd in the Catalan-Spanish pair.
A3-108 Machine Translation System for Similar Language Translation Shared Task 2020
Authors: Saumitra Yadav, Manish Shrivastava
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

In this paper, we describe our submissions for Similar Language Translation Shared Task 2020. We built 12 systems in each direction for Hindi⇐⇒Marathi language pair. This paper outlines initial baseline experiments with various tokenization schemes to train statistical models. Using optimal tokenization scheme among these we created synthetic source side text with back translation. And prune synthetic text with language model scores. This synthetic data was then used along with training data in various settings to build translation models. We also report configuration of the submitted systems and results produced by them.
The University of Maryland’s Submissions to the WMT20 Chat Translation Task: Searching for More Data to Adapt Discourse-Aware Neural Machine Translation
Authors: Calvin Bao, Yow-Ting Shiue, Chujun Song, Jie Li, Marine Carpuat
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Machine Translation, Translation

This paper describes the University of Maryland’s submissions to the WMT20 Shared Task on Chat Translation. We focus on translating agent-side utterances from English to German. We started from an off-the-shelf BPE-based standard transformer model trained with WMT17 news and fine-tuned it with the provided in-domain training data. In addition, we augment the training set with its best matches in the WMT19 news dataset. Our primary submission uses a standard Transformer, while our contrastive submissions use multi-encoder Transformers to attend to previous utterances. Our primary submission achieves 56.7 BLEU on the agent side (en→de), outperforming a baseline system provided by the task organizers by more than 13 BLEU points. Moreover, according to an evaluation on a set of carefully-designed examples, the multi-encoder architecture is able to generate more coherent translations.
Naver Labs Europe’s Participation in the Robustness, Chat, and Biomedical Tasks at WMT 2020
Authors: Alexandre Berard, Ioan Calapodescu, Vassilina Nikoulina, Jerin Philip
Date: 2020-11-01 00:00:00
Proceeding: WMT (EMNLP) 2020 11
Tasks: Language Modeling, Language Modelling, Translation

This paper describes Naver Labs Europe’s participation in the Robustness, Chat, and Biomedical Translation tasks at WMT 2020. We propose a bidirectional German-English model that is multi-domain, robust to noise, and which can translate entire documents (or bilingual dialogues) at once. We use the same ensemble of such models as our primary submission to all three tasks and achieve competitive results. We also experiment with language model pre-training techniques and evaluate their impact on robustness to noise and out-of-domain translation. For German, Spanish, Italian, and French to English translation in the Biomedical Task, we also submit our recently released multilingual Covid19NMT model.