ymoslem/acl-abstracts huggingface dataset:
The University of Maryland`s Kazakh-English Neural Machine Translation System at WMT19 (2019)

This paper describes the University of Maryland`s submission to the WMT 2019 Kazakh-English news translation task. We study the impact of transfer learning from another low-resource but related language. We experiment with different ways of encoding lexical units to maximize lexical overlap between the two language pairs, as well as back-translation and ensembling. The submitted system improves over a Kazakh-only baseline by +5.45 BLEU on newstest2019.
DBMS-KU Interpolation for WMT19 News Translation Task (2019)

This paper presents the participation of DBMS-KU Interpolation system in WMT19 shared task, namely, Kazakh-English language pair. We examine the use of interpolation method using a different language model order. Our Interpolation system combines a direct translation with Russian as a pivot language. We use 3-gram and 5-gram language model orders to perform the language translation in this work. To reduce noise in the pivot translation process, we prune the phrase table of source-pivot and pivot-target. Our experimental results show that our Interpolation system outperforms the Baseline in terms of BLEU-cased score by +0.5 and +0.1 points in Kazakh-English and English-Kazakh, respectively. In particular, using the 5-gram language model order in our system could obtain better BLEU-cased score than utilizing the 3-gram one. Interestingly, we found that by employing the Interpolation system could reduce the perplexity score of English-Kazakh when using 3-gram language model order.
Lingua Custodia at WMT`19: Attempts to Control Terminology (2019)

This paper describes Lingua Custodia`s submission to the WMT`19 news shared task for German-to-French on the topic of the EU elections. We report experiments on the adaptation of the terminology of a machine translation system to a specific topic, aimed at providing more accurate translations of specific entities like political parties and person names, given that the shared task provided no in-domain training parallel data dealing with the restricted topic. Our primary submission to the shared task uses backtranslation generated with a type of decoding allowing the insertion of constraints in the output in order to guarantee the correct translation of specific terms that are not necessarily observed in the data.
The TALP-UPC Machine Translation Systems for WMT19 News Translation Task: Pivoting Techniques for Low Resource MT (2019)

In this article, we describe the TALP-UPC research group participation in the WMT19 news translation shared task for Kazakh-English. Given the low amount of parallel training data, we resort to using Russian as pivot language, training subword-based statistical translation systems for Russian-Kazakh and Russian-English that were then used to create two synthetic pseudo-parallel corpora for Kazakh-English and English-Kazakh respectively. Finally, a self-attention model based on the decoder part of the Transformer architecture was trained on the two pseudo-parallel corpora.
Kyoto University Participation to the WMT 2019 News Shared Task (2019)

We describe here the experiments we did for the the news translation shared task of WMT 2019. We focused on the new German-to-French language direction, and mostly used current standard approaches to develop a Neural Machine Translation system. We make use of the Tensor2Tensor implementation of the Transformer model. After carefully cleaning the data and noting the importance of the good use of recent monolingual data for the task, we obtain our final result by combining the output of a diverse set of trained models through the use of their {\textquotedblleft}checkpoint agreement{\textquotedblright}.
NICT`s Supervised Neural Machine Translation Systems for the WMT19 News Translation Task (2019)

In this paper, we describe our supervised neural machine translation (NMT) systems that we developed for the news translation task for Kazakh{\ensuremath{\leftrightarrow}}English, Gujarati{\ensuremath{\leftrightarrow}}English, Chinese{\ensuremath{\leftrightarrow}}English, and English{\textrightarrow}Finnish translation directions. We focused on leveraging multilingual transfer learning and back-translation for the extremely low-resource language pairs: Kazakh{\ensuremath{\leftrightarrow}}English and Gujarati{\ensuremath{\leftrightarrow}}English translation. For the Chinese{\ensuremath{\leftrightarrow}}English translation, we used the provided parallel data augmented with a large quantity of back-translated monolingual data to train state-of-the-art NMT systems. We then employed techniques that have been proven to be most effective, such as back-translation, fine-tuning, and model ensembling, to generate the primary submissions of Chinese{\ensuremath{\leftrightarrow}}English. For English{\textrightarrow}Finnish, our submission from WMT18 remains a strong baseline despite the increase in parallel corpora for this year`s task.
The University of Sydney`s Machine Translation System for WMT19 (2019)

This paper describes the University of Sydney`s submission of the WMT 2019 shared news translation task. We participated in the Finnish-{\ensuremath{>}}English direction and got the best BLEU(33.0) score among all the participants. Our system is based on the self-attentional Transformer networks, into which we integrated the most recent effective strategies from academic research (e.g., BPE, back translation, multi-features data selection, data augmentation, greedy model ensemble, reranking, ConMBR system combination, and postprocessing). Furthermore, we propose a novel augmentation method Cycle Translation and a data mixture strategy Big/Small parallel construction to entirely exploit the synthetic corpus. Extensive experiments show that adding the above techniques can make continuous improvements of the BLEU scores, and the best result outperforms the baseline (Transformer ensemble model trained with the original parallel corpus) by approximately 5.3 BLEU score, achieving the state-of-the-art performance.
UdS-DFKI Participation at WMT 2019: Low-Resource (en-gu) and Coreference-Aware (en-de) Systems (2019)

This paper describes the UdS-DFKI submission to the WMT2019 news translation task for Gujarati{--}English (low-resourced pair) and German{--}English (document-level evaluation). Our systems rely on the on-line extraction of parallel sentences from comparable corpora for the first scenario and on the inclusion of coreference-related information in the training data in the second one.
The IIIT-H Gujarati-English Machine Translation System for WMT19 (2019)

This paper describes the Neural Machine Translation system of IIIT-Hyderabad for the Gujarati{\textrightarrow}English news translation shared task of WMT19. Our system is basedon encoder-decoder framework with attention mechanism. We experimented with Multilingual Neural MT models. Our experiments show that Multilingual Neural Machine Translation leveraging parallel data from related language pairs helps in significant BLEU improvements upto 11.5, for low resource language pairs like Gujarati-English
Kingsoft`s Neural Machine Translation System for WMT19 (2019)

This paper describes the Kingsoft AI Lab`s submission to the WMT2019 news translation shared task. We participated in two language directions: English-Chinese and Chinese-English. For both language directions, we trained several variants of Transformer models using the provided parallel data enlarged with a large quantity of back-translated monolingual data. The best translation result was obtained with ensemble and reranking techniques. According to automatic metrics (BLEU) our Chinese-English system reached the second highest score, and our English-Chinese system reached the second highest score for this subtask.
The AFRL WMT19 Systems: Old Favorites and New Tricks (2019)

This paper describes the Air Force Research Laboratory (AFRL) machine translation systems and the improvements that were developed during the WMT19 evaluation campaign. This year, we refine our approach to training popular neural machine translation toolkits, experiment with a new domain adaptation technique and again measure improvements in performance on the Russian{--}English language pair.
Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models (2019)

We study several methods for full or partial sharing of the decoder parameters of multi-lingual NMT models. Using only the WMT 2019 shared task parallel datasets for training, we evaluate both fully supervised and zero-shot translation performance in 110 unique translation directions. We use additional test sets and re-purpose evaluation methods recently used for unsupervised MT in order to evaluate zero-shot translation performance for language pairs where no gold-standard parallel data is available. To our knowledge, this is the largest evaluation of multi-lingual translation yet conducted in terms of the total size of the training data we use, and in terms of the number of zero-shot translation pairs we evaluate. We conduct an in-depth evaluation of the translation performance of different models, highlighting the trade-offs between methods of sharing decoder parameters. We find that models which have task-specific decoder parameters outperform models where decoder parameters are fully shared across all tasks.
The MLLP-UPV Supervised Machine Translation Systems for WMT19 News Translation Task (2019)

This paper describes the participation of the MLLP research group of the Universitat Polit{\`e}cnica de Val{\`e}ncia in the WMT 2019 News Translation Shared Task. In this edition, we have submitted systems for the German {\ensuremath{\leftrightarrow}} English and German {\ensuremath{\leftrightarrow}} French language pairs, participating in both directions of each pair. Our submitted systems, based on the Transformer architecture, make ample use of data filtering, synthetic data and domain adaptation through fine-tuning.
Microsoft Translator at WMT 2019: Towards Large-Scale Document-Level Neural Machine Translation (2019)

This paper describes the Microsoft Translator submissions to the WMT19 news translation shared task for English-German. Our main focus is document-level neural machine translation with deep transformer models. We start with strong sentence-level baselines, trained on large-scale data created via data-filtering and noisy back-translation and find that back-translation seems to mainly help with translationese input. We explore fine-tuning techniques, deeper models and different ensembling strategies to counter these effects. Using document boundaries present in the authentic and synthetic parallel data, we create sequences of up to 1000 subword segments and train transformer translation models. We experiment with data augmentation techniques for the smaller authentic data with document-boundaries and for larger authentic data without boundaries. We further explore multi-task training for the incorporation of document-level source language monolingual data via the BERT-objective on the encoder and two-pass decoding for combinations of sentence-level and document-level systems. Based on preliminary human evaluation results, evaluators strongly prefer the document-level systems over our comparable sentence-level system. The document-level systems also seem to score higher than the human references in source-based direct assessment.
CUNI Submission for Low-Resource Languages in WMT News 2019 (2019)

This paper describes the CUNI submission to the WMT 2019 News Translation Shared Task for the low-resource languages: Gujarati-English and Kazakh-English. We participated in both language pairs in both translation directions. Our system combines transfer learning from a different high-resource language pair followed by training on backtranslated monolingual data. Thanks to the simultaneous training in both directions, we can iterate the backtranslation process. We are using the Transformer model in a constrained submission.
CUNI Systems for the Unsupervised News Translation Task in WMT 2019 (2019)

In this paper we describe the CUNI translation system used for the unsupervised news shared task of the ACL 2019 Fourth Conference on Machine Translation (WMT19). We follow the strategy of Artetxe ae at. (2018b), creating a seed phrase-based system where the phrase table is initialized from cross-lingual embedding mappings trained on monolingual data, followed by a neural machine translation system trained on synthetic parallel data. The synthetic corpus was produced from a monolingual corpus by a tuned PBMT model refined through iterative back-translation. We further focus on the handling of named entities, i.e. the part of vocabulary where the cross-lingual embedding mapping suffers most. Our system reaches a BLEU score of 15.3 on the German-Czech WMT19 shared task.
A Comparison on Fine-grained Pre-trained Embeddings for the WMT19Chinese-English News Translation Task (2019)

This paper describes our submission to the WMT 2019 Chinese-English (zh-en) news translation shared task. Our systems are based on RNN architectures with pre-trained embeddings which utilize character and sub-character information. We compare models with these different granularity levels using different evaluating metics. We find that a finer granularity embeddings can help the model according to character level evaluation and that the pre-trained embeddings can also be beneficial for model performance marginally when the training data is limited.
The NiuTrans Machine Translation Systems for WMT19 (2019)

This paper described NiuTrans neural machine translation systems for the WMT 2019 news translation tasks. We participated in 13 translation directions, including 11 supervised tasks, namely EN{\ensuremath{\leftrightarrow}}{\{}ZH, DE, RU, KK, LT{\}}, GU{\textrightarrow}EN and the unsupervised DE{\ensuremath{\leftrightarrow}}CS sub-track. Our systems were built on Deep Transformer and several back-translation methods. Iterative knowledge distillation and ensemble+reranking were also employed to obtain stronger models. Our unsupervised submissions were based on NMT enhanced by SMT. As a result, we achieved the highest BLEU scores in {\{}KK{\ensuremath{\leftrightarrow}}EN, GU{\textrightarrow}EN{\}} directions, ranking 2nd in {\{}RU{\textrightarrow}EN, DE{\ensuremath{\leftrightarrow}}CS{\}} and 3rd in {\{}ZH{\textrightarrow}EN, LT{\textrightarrow}EN, EN{\textrightarrow}RU, EN{\ensuremath{\leftrightarrow}}DE{\}} among all constrained submissions.
Multi-Source Transformer for Kazakh-Russian-English Neural Machine Translation (2019)

We describe the neural machine translation (NMT) system developed at the National Research Council of Canada (NRC) for the Kazakh-English news translation task of the Fourth Conference on Machine Translation (WMT19). Our submission is a multi-source NMT taking both the original Kazakh sentence and its Russian translation as input for translating into English.
Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring (2019)

This paper describes CAiRE`s submission to the unsupervised machine translation track of the WMT`19 news shared task from German to Czech. We leverage a phrase-based statistical machine translation (PBSMT) model and a pre-trained language model to combine word-level neural machine translation (NMT) and subword-level NMT models without using any parallel data. We propose to solve the morphological richness problem of languages by training byte-pair encoding (BPE) embeddings for German and Czech separately, and they are aligned using MUSE (Conneau et al., 2018). To ensure the fluency and consistency of translations, a rescoring mechanism is proposed that reuses the pre-trained language model to select the translation candidates generated through beam search. Moreover, a series of pre-processing and post-processing approaches are applied to improve the quality of final translations.
JUMT at WMT2019 News Translation Task: A Hybrid Approach to Machine Translation for Lithuanian to English (2019)

In the current work, we present a description of the system submitted to WMT 2019 News Translation Shared task. The system was created to translate news text from Lithuanian to English. To accomplish the given task, our system used a Word Embedding based Neural Machine Translation model to post edit the outputs generated by a Statistical Machine Translation model. The current paper documents the architecture of our model, descriptions of the various modules and the results produced using the same. Our system garnered a BLEU score of 17.6.
Johns Hopkins University Submission for WMT News Translation Task (2019)

We describe the work of Johns Hopkins University for the shared task of news translation organized by the Fourth Conference on Machine Translation (2019). We submitted systems for both directions of the English-German language pair. The systems combine multiple techniques {--} sampling, filtering, iterative backtranslation, and continued training {--} previously used to improve performance of neural machine translation models. At submission time, we achieve a BLEU score of 38.1 for De-En and 42.5 for En-De translation directions on newstest2019. Post-submission, the score is 38.4 for De-En and 42.8 for En-De. Various experiments conducted in the process are also described.
NICT`s Unsupervised Neural and Statistical Machine Translation Systems for the WMT19 News Translation Task (2019)

This paper presents the NICT`s participation in the WMT19 unsupervised news translation task. We participated in the unsupervised translation direction: German-Czech. Our primary submission to the task is the result of a simple combination of our unsupervised neural and statistical machine translation systems. Our system is ranked first for the German-to-Czech translation task, using only the data provided by the organizers ({\textquotedblleft}constraint'{\textquotedblright}), according to both BLEU-cased and human evaluation. We also performed contrastive experiments with other language pairs, namely, English-Gujarati and English-Kazakh, to better assess the effectiveness of unsupervised machine translation in for distant language pairs and in truly low-resource conditions.
PROMT Systems for WMT 2019 Shared Translation Task (2019)

This paper describes the PROMT submissions for the WMT 2019 Shared News Translation Task. This year we participated in two language pairs and in three directions: English-Russian, English-German and German-English. All our submissions are Marian-based neural systems. We use significantly more data compared to the last year. We also present our improved data filtering pipeline.
JU-Saarland Submission to the WMT2019 English--Gujarati Translation Shared Task (2019)

In this paper we describe our joint submission (JU-Saarland) from Jadavpur University and Saarland University in the WMT 2019 news translation shared task for English{--}Gujarati language pair within the translation task sub-track. Our baseline and primary submissions are built using Recurrent neural network (RNN) based neural machine translation (NMT) system which follows attention mechanism. Given the fact that the two languages belong to different language families and there is not enough parallel data for this language pair, building a high quality NMT system for this language pair is a difficult task. We produced synthetic data through back-translation from available monolingual data. We report the translation quality of our English{--}Gujarati and Gujarati{--}English NMT systems trained at word, byte-pair and character encoding levels where RNN at word level is considered as the baseline and used for comparison purpose. Our English{--}Gujarati system ranked in the second position in the shared task.
Facebook FAIR`s WMT19 News Translation Task Submission (2019)

This paper describes Facebook FAIR`s submission to the WMT19 shared news translation task. We participate in four language directions, English {\ensuremath{<}}-{\ensuremath{>}} German and English {\ensuremath{<}}-{\ensuremath{>}} Russian in both directions. Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the FAIRSEQ sequence modeling toolkit. This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking. Our system improves on our previous system`s performance by 4.5 BLEU points and achieves the best case-sensitive BLEU score for the translation direction English{\textrightarrow}Russian.
eTranslation`s Submissions to the WMT 2019 News Translation Task (2019)

This paper describes the submissions of the eTranslation team to the WMT 2019 news translation shared task. The systems have been developed with the aim of identifying and following rather than establishing best practices, under the constraints imposed by a low resource training and decoding environment normally used for our production systems. Thus most of the findings and results are transferable to systems used in the eTranslation service. Evaluations suggest that this approach is able to produce decent models with good performance and speed without the overhead of using prohibitively deep and complex architectures.
Tilde`s Machine Translation Systems for WMT 2019 (2019)

The paper describes the development process of Tilde`s NMT systems for the WMT 2019 shared task on news translation. We trained systems for the English-Lithuanian and Lithuanian-English translation directions in constrained and unconstrained tracks. We build upon the best methods of the previous year`s competition and combine them with recent advancements in the field. We also present a new method to ensure source domain adherence in back-translated data. Our systems achieved a shared first place in human evaluation.
Apertium-fin-eng--Rule-based Shallow Machine Translation for WMT 2019 Shared Task (2019)

In this paper we describe a rule-based, bi-directional machine translation system for the Finnish{---}English language pair. The baseline system was based on the existing data of FinnWordNet, omorfi and apertium-eng. We have built the disambiguation, lexical selection and translation rules by hand. The dictionaries and rules have been developed based on the shared task data. We describe in this article the use of the shared task data as a kind of a test-driven development workflow in RBMT development and show that it suits perfectly to a modern software engineering continuous integration workflow of RBMT and yields big increases to BLEU scores with minimal effort.
English-Czech Systems in WMT19: Document-Level Transformer (2019)

We describe our NMT systems submitted to the WMT19 shared task in English{\textrightarrow}Czech news translation. Our systems are based on the Transformer model implemented in either Tensor2Tensor (T2T) or Marian framework. We aimed at improving the adequacy and coherence of translated documents by enlarging the context of the source and target. Instead of translating each sentence independently, we split the document into possibly overlapping multi-sentence segments. In case of the T2T implementation, this {\textquotedblleft}document-level{\textquotedblright}-trained system achieves a +0.6 BLEU improvement (p {\ensuremath{<}} 0.05) relative to the same system applied on isolated sentences. To assess the potential effect document-level models might have on lexical coherence, we performed a semi-automatic analysis, which revealed only a few sentences improved in this aspect. Thus, we cannot draw any conclusions from this week evidence.