ymoslem/acl-abstracts huggingface dataset:
The TALP-UPC System Description for WMT20 News Translation Task: Multilingual Adaptation for Low Resource MT (2020)

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 (2020)

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 (2020)

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 {\ensuremath{<}}{--}{\ensuremath{>}} German and English {\ensuremath{<}}{--}{\ensuremath{>}} 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 (2020)

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 (2020)

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.
Tilde at WMT 2020: News Task Systems (2020)

This paper describes Tilde`s submission to the WMT2020 shared task on news translation for both directions of the English-Polish language pair in both the constrained and the unconstrained tracks. We follow our submissions form the previous years and build our baseline systems to be morphologically motivated sub-word unit-based Transformer base models that we train using the Marian machine translation toolkit. Additionally, we experiment with different parallel and monolingual data selection schemes, as well as sampled back-translation. Our final models are ensembles of Transformer base and Transformer big models which feature right-to-left re-ranking.
Samsung R\&D Institute Poland submission to WMT20 News Translation Task (2020)

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 {\textrightarrow} 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 (2020)

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 {\ensuremath{\leftrightarrow}} 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 (2020)

This paper describes the University of Edinburgh`s submission of German {\ensuremath{<}}-{\ensuremath{>}} 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 (2020)

We describe our submission for the English{\textrightarrow}Tamil and Tamil{\textrightarrow}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 (2020)

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.
The Ubiqus English-Inuktitut System for WMT20 (2020)

This paper describes Ubiqus' submission to the WMT20 English-Inuktitut shared news translation task. Our main system, and only submission, is based on a multilingual approach, jointly training a Transformer model on several agglutinative languages. The English-Inuktitut translation task is challenging at every step, from data selection, preparation and tokenization to quality evaluation down the line. Difficulties emerge both because of the peculiarities of the Inuktitut language as well as the low-resource context.
SJTU-NICT`s Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task (2020)

In this paper, we introduced our joint team SJTU-NICT {\textquoteleft}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 (2020)

This paper presents neural machine translation systems and their combination built for the WMT20 English-Polish and Japanese-{\ensuremath{>}}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.
WeChat Neural Machine Translation Systems for WMT20 (2020)

We participate in the WMT 2020 shared newstranslation task on Chinese{\textrightarrow}English. Our system is based on the Transformer (Vaswaniet al., 2017a) with effective variants and the DTMT (Meng and Zhang, 2019) architecture. In our experiments, we employ data selection, several synthetic data generation approaches (i.e., back-translation, knowledge distillation, and iterative in-domain knowledge transfer), advanced finetuning approaches and self-bleu based model ensemble. Our constrained Chinese{\textrightarrow}English system achieves 36.9 case-sensitive BLEU score, which is thehighest among all submissions.
PROMT Systems for WMT 2020 Shared News Translation Task (2020)

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 (2020)

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 (2020)

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 (2020)

We describe our two NMT systems submitted to the WMT 2020 shared task in English{\ensuremath{<}}-{\ensuremath{>}}Czech and English{\ensuremath{<}}-{\ensuremath{>}}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 (2020)

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 (2020)

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).
HW-TSC`s Participation in the WMT 2020 News Translation Shared Task (2020)

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 (2020)

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.
The Volctrans Machine Translation System for WMT20 (2020)

This paper describes our submission systems for VolcTrans for WMT20 shared news translation task. We participated in 8 translation directions. Our basic systems are based on Transformer (CITATION), into which we also employed new architectures (bigger or deeper Transformers, dynamic convolution). The final systems include text pre-process, subword(a.k.a. BPE(CITATION)), baseline model training, iterative back-translation, model ensemble, knowledge distillation and multilingual pre-training.
Tencent Neural Machine Translation Systems for the WMT20 News Translation Task (2020)

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 (2020)

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 (2020)

This paper describes the DeepMind submission to the Chinese$\rightarrow$English 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 (2020)

This paper describes NiuTrans neural machine translation systems of the WMT20 news translation tasks. We participated in Japanese{\ensuremath{<}}-{\ensuremath{>}}English, English-{\ensuremath{>}}Chinese, Inuktitut-{\ensuremath{>}}English and Tamil-{\ensuremath{>}}English total five tasks and rank first in Japanese{\ensuremath{<}}-{\ensuremath{>}}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-{\ensuremath{>}}English and Tamil-{\ensuremath{>}}English tasks, we built multilingual models separately and employed pretraining word embedding to obtain better performance.
Fine-grained linguistic evaluation for state-of-the-art Machine Translation (2020)

This paper describes a test suite submission providing detailed statistics of linguistic performance for the state-of-the-art German-English systems of the Fifth Conference of Machine Translation (WMT20). The analysis covers 107 phenomena organized in 14 categories based on about 5,500 test items, including a manual annotation effort of 45 person hours. Two systems (Tohoku and Huoshan) appear to have significantly better test suite accuracy than the others, although the best system of WMT20 is not significantly better than the one from WMT19 in a macro-average. Additionally, we identify some linguistic phenomena where all systems suffer (such as idioms, resultative predicates and pluperfect), but we are also able to identify particular weaknesses for individual systems (such as quotation marks, lexical ambiguity and sluicing). Most of the systems of WMT19 which submitted new versions this year show improvements.
Gender Coreference and Bias Evaluation at WMT 2020 (2020)

Gender bias in machine translation can manifest when choosing gender inflections based on spurious gender correlations. For example, always translating doctors as men and nurses as women. This can be particularly harmful as models become more popular and deployed within commercial systems. Our work presents the largest evidence for the phenomenon in more than 19 systems submitted to the WMT over four diverse target languages: Czech, German, Polish, and Russian. To achieve this, we use WinoMT, a recent automatic test suite which examines gender coreference and bias when translating from English to languages with grammatical gender. We extend WinoMT to handle two new languages tested in WMT: Polish and Czech. We find that all systems consistently use spurious correlations in the data rather than meaningful contextual information.
The MUCOW word sense disambiguation test suite at WMT 2020 (2020)

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 -{\ensuremath{>}} Czech, English -{\ensuremath{>}} German, and English -{\ensuremath{>}} 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 (2020)

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.