pwc-archive/papers-with-abstracts huggingface dataset:
Findings of the Third Shared Task on Multimodal Machine Translation
Authors: Lo{\"\i}c Barrault, Fethi Bougares, Lucia Specia, Chiraag Lala, Desmond Elliott, Stella Frank
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Multimodal Machine Translation, Sentence, Translation

We present the results from the third shared task on multimodal machine translation. In this task a source sentence in English is supplemented by an image and participating systems are required to generate a translation for such a sentence into German, French or Czech. The image can be used in addition to (or instead of) the source sentence. This year the task was extended with a third target language (Czech) and a new test set. In addition, a variant of this task was introduced with its own test set where the source sentence is given in multiple languages: English, French and German, and participating systems are required to generate a translation in Czech. Seven teams submitted 45 different systems to the two variants of the task. Compared to last year, the performance of the multimodal submissions improved, but text-only systems remain competitive.
Findings of the WMT 2018 Biomedical Translation Shared Task: Evaluation on Medline test sets
Authors: Mariana Neves, Antonio Jimeno Yepes, Aur{\'e}lie N{\'e}v{\'e}ol, Cristian Grozea, Amy Siu, Madeleine Kittner, Karin Verspoor
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

Machine translation enables the automatic translation of textual documents between languages and can facilitate access to information only available in a given language for non-speakers of this language, e.g. research results presented in scientific publications. In this paper, we provide an overview of the Biomedical Translation shared task in the Workshop on Machine Translation (WMT) 2018, which specifically examined the performance of machine translation systems for biomedical texts. This year, we provided test sets of scientific publications from two sources (EDP and Medline) and for six language pairs (English with each of Chinese, French, German, Portuguese, Romanian and Spanish). We describe the development of the various test sets, the submissions that we received and the evaluations that we carried out. We obtained a total of 39 runs from six teams and some of this year{'}s BLEU scores were somewhat higher that last year{'}s, especially for teams that made use of biomedical resources or state-of-the-art MT algorithms (e.g. Transformer). Finally, our manual evaluation scored automatic translations higher than the reference translations for German and Spanish.
An Empirical Study of Machine Translation for the Shared Task of WMT18
Authors: Chao Bei, Hao Zong, Yiming Wang, Baoyong Fan, Shiqi Li, Conghu Yuan
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Chinese Word Segmentation, Language Modeling, Language Modelling, Machine Translation, Translation

This paper describes the Global Tone Communication Co., Ltd.{'}s submission of the WMT18 shared news translation task. We participated in the English-to-Chinese direction and get the best BLEU (43.8) scores among all the participants. The submitted system focus on data clearing and techniques to build a competitive model for this task. Unlike other participants, the submitted system are mainly relied on the data filtering to obtain the best BLEU score. We do data filtering not only for provided sentences but also for the back translated sentences. The techniques we apply for data filtering include filtering by rules, language models and translation models. We also conduct several experiments to validate the effectiveness of training techniques. According to our experiments, the Annealing Adam optimizing function and ensemble decoding are the most effective techniques for the model training.
Robust parfda Statistical Machine Translation Results
Authors: Ergun Bi{\c{c}}ici
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

We build parallel feature decay algorithms (parfda) Moses statistical machine translation (SMT) models for language pairs in the translation task. parfda obtains results close to the top constrained phrase-based SMT with an average of 2.252 BLEU points difference on WMT 2017 datasets using significantly less computation for building SMT systems than that would be spent using all available corpora. We obtain BLEU upper bounds based on target coverage to identify which systems used additional data. We use PRO for tuning to decrease fluctuations in the results and postprocess translation outputs to decrease translation errors due to the casing of words. F1 scores on the key phrases of the English to Turkish testsuite that we prepared reveal that parfda achieves 2nd best results. Truecasing translations before scoring obtained the best results overall.
The TALP-UPC Machine Translation Systems for WMT18 News Shared Translation Task
Authors: Noe Casas, Carlos Escolano, Marta R. Costa-juss{\`a}, Jos{\'e} A. R. Fonollosa
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

In this article we describe the TALP-UPC research group participation in the WMT18 news shared translation task for Finnish-English and Estonian-English within the multi-lingual subtrack. All of our primary submissions implement an attention-based Neural Machine Translation architecture. Given that Finnish and Estonian belong to the same language family and are similar, we use as training data the combination of the datasets of both language pairs to paliate the data scarceness of each individual pair. We also report the translation quality of systems trained on individual language pair data to serve as baseline and comparison reference.
Phrase-based Unsupervised Machine Translation with Compositional Phrase Embeddings
Authors: Maksym Del, Andre T{\"a}ttar, Mark Fishel
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation, Unsupervised Machine Translation

This paper describes the University of Tartu{'}s submission to the unsupervised machine translation track of WMT18 news translation shared task. We build several baseline translation systems for both directions of the English-Estonian language pair using monolingual data only; the systems belong to the phrase-based unsupervised machine translation paradigm where we experimented with phrase lengths of up to 3. As a main contribution, we performed a set of standalone experiments with compositional phrase embeddings as a substitute for phrases as individual vocabulary entries. Results show that reasonable n-gram vectors can be obtained by simply summing up individual word vectors which retains or improves the performance of phrase-based unsupervised machine tranlation systems while avoiding limitations of atomic phrase vectors.
Alibaba's Neural Machine Translation Systems for WMT18
Authors: Yongchao Deng, Shanbo Cheng, Jun Lu, Kai Song, Jingang Wang, Shenglan Wu, Liang Yao, Guchun Zhang, Haibo Zhang, Pei Zhang, Changfeng Zhu, Boxing Chen
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Reranking, Translation

This paper describes the submission systems of Alibaba for WMT18 shared news translation task. We participated in 5 translation directions including English ↔ Russian, English ↔ Turkish in both directions and English → Chinese. Our systems are based on Google{'}s Transformer model architecture, into which we integrated the most recent features from the academic research. We also employed most techniques that have been proven effective during the past WMT years, such as BPE, back translation, data selection, model ensembling and reranking, at industrial scale. For some morphologically-rich languages, we also incorporated linguistic knowledge into our neural network. For the translation tasks in which we have participated, our resulting systems achieved the best case sensitive BLEU score in all 5 directions. Notably, our English → Russian system outperformed the second reranked system by 5 BLEU score.
The RWTH Aachen University English-German and German-English Unsupervised Neural Machine Translation Systems for WMT 2018
Authors: Miguel Gra{\c{c}}a, Yunsu Kim, Julian Schamper, Jiahui Geng, Hermann Ney
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Decoder, Machine Translation, NMT, Translation, Word Embeddings

This paper describes the unsupervised neural machine translation (NMT) systems of the RWTH Aachen University developed for the English â†” German news translation task of the \textit{EMNLP 2018 Third Conference on Machine Translation} (WMT 2018). Our work is based on iterative back-translation using a shared encoder-decoder NMT model. We extensively compare different vocabulary types, word embedding initialization schemes and optimization methods for our model. We also investigate gating and weight normalization for the word embedding layer.
The AFRL WMT18 Systems: Ensembling, Continuation and Combination
Authors: Jeremy Gwinnup, Tim Anderson, Grant Erdmann, Katherine Young
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

This paper describes the Air Force Research Laboratory (AFRL) machine translation systems and the improvements that were developed during the WMT18 evaluation campaign. This year, we examined the developments and additions to popular neural machine translation toolkits and measure improvements in performance on the Russian{--}English language pair.
The University of Edinburgh's Submissions to the WMT18 News Translation Task
Authors: Barry Haddow, Nikolay Bogoychev, Denis Emelin, Ulrich Germann, Roman Grundkiewicz, Kenneth Heafield, Antonio Valerio Miceli Barone, Rico Sennrich
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

The University of Edinburgh made submissions to all 14 language pairs in the news translation task, with strong performances in most pairs. We introduce new RNN-variant, mixed RNN/Transformer ensembles, data selection and weighting, and extensions to back-translation.
TencentFmRD Neural Machine Translation for WMT18
Authors: Bojie Hu, Ambyer Han, Shen Huang
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, NMT, Reranking, Translation

This paper describes the Neural Machine Translation (NMT) system of TencentFmRD for Chinese↔English news translation tasks of WMT 2018. Our systems are neural machine translation systems trained with our original system TenTrans. TenTrans is an improved NMT system based on Transformer self-attention mechanism. In addition to the basic settings of Transformer training, TenTrans uses multi-model fusion techniques, multiple features reranking, different segmentation models and joint learning. Finally, we adopt some data selection strategies to fine-tune the trained system and achieve a stable performance improvement. Our Chinese→English system achieved the second best BLEU scores and fourth best cased BLEU scores among all WMT18 submitted systems.
The MLLP-UPV German-English Machine Translation System for WMT18
Authors: Javier Iranzo-S{\'a}nchez, Pau Baquero-Arnal, Gon{\c{c}}al V. Garc{\'e}s D{\'\i}az-Mun{\'\i}o, Adri{\`a} Mart{\'\i}nez-Villaronga, Jorge Civera, Alfons Juan
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Data Augmentation, Machine Translation, Translation

This paper describes the statistical machine translation system built by the MLLP research group of Universitat Polit{\`e}cnica de Val{\`e}ncia for the Germanâ†’English news translation shared task of the EMNLP 2018 Third Conference on Machine Translation (WMT18). We used an ensemble of Transformer architecture{--}based neural machine translation systems. To train our system under {``}constrained{''} conditions, we filtered the provided parallel data with a scoring technique using character-based language models, and we added parallel data based on synthetic source sentences generated from the provided monolingual corpora.
CUNI Submissions in WMT18
Authors: Tom Kocmi, Roman Sudarikov, Ond{\v{r}}ej Bojar
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

We participated in the WMT 2018 shared news translation task in three language pairs: English-Estonian, English-Finnish, and English-Czech. Our main focus was the low-resource language pair of Estonian and English for which we utilized Finnish parallel data in a simple method. We first train a {``}parent model{''} for the high-resource language pair followed by adaptation on the related low-resource language pair. This approach brings a substantial performance boost over the baseline system trained only on Estonian-English parallel data. Our systems are based on the Transformer architecture. For the English to Czech translation, we have evaluated our last year models of hybrid phrase-based approach and neural machine translation mainly for comparison purposes.
The JHU Machine Translation Systems for WMT 2018
Authors: Philipp Koehn, Kevin Duh, Brian Thompson
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

We report on the efforts of the Johns Hopkins University to develop neural machine translation systems for the shared task for news translation organized around the Conference for Machine Translation (WMT) 2018. We developed systems for German{--}English, English{--} German, and Russian{--}English. Our novel contributions are iterative back-translation and fine-tuning on test sets from prior years.
PROMT Systems for WMT 2018 Shared Translation Task
Authors: Alex Molchanov, er
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

This paper describes the PROMT submissions for the WMT 2018 Shared News Translation Task. This year we participated only in the English-Russian language pair. We built two primary neural networks-based systems: 1) a pure Marian-based neural system and 2) a hybrid system which incorporates OpenNMT-based neural post-editing component into our RBMT engine. We also submitted pure rule-based translation (RBMT) for contrast. We show competitive results with both primary submissions which significantly outperform the RBMT baseline.
NTT's Neural Machine Translation Systems for WMT 2018
Authors: Makoto Morishita, Jun Suzuki, Masaaki Nagata
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Re-Ranking, Translation

This paper describes NTT{'}s neural machine translation systems submitted to the WMT 2018 English-German and German-English news translation tasks. Our submission has three main components: the Transformer model, corpus cleaning, and right-to-left n-best re-ranking techniques. Through our experiments, we identified two keys for improving accuracy: filtering noisy training sentences and right-to-left re-ranking. We also found that the Transformer model requires more training data than the RNN-based model, and the RNN-based model sometimes achieves better accuracy than the Transformer model when the corpus is small.
The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2018
Authors: Ngoc-Quan Pham, Jan Niehues, Alex Waibel, er
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Decoder, GPU, Machine Translation, Sentence, Translation

We present our experiments in the scope of the news translation task in WMT 2018, in directions: Englishâ†’German. The core of our systems is the encoder-decoder based neural machine translation models using the transformer architecture. We enhanced the model with a deeper architecture. By using techniques to limit the memory consumption, we were able to train models that are 4 times larger on one GPU and improve the performance by 1.2 BLEU points. Furthermore, we performed sentence selection for the newly available ParaCrawl corpus. Thereby, we could improve the effectiveness of the corpus by 0.5 BLEU points.
Tilde's Machine Translation Systems for WMT 2018
Authors: M{\=a}rcis Pinnis, Mat{\=\i}ss Rikters, Rihards Kri{\v{s}}lauks
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, NMT, Translation

The paper describes the development process of the Tilde{'}s NMT systems that were submitted for the WMT 2018 shared task on news translation. We describe the data filtering and pre-processing workflows, the NMT system training architectures, and automatic evaluation results. For the WMT 2018 shared task, we submitted seven systems (both constrained and unconstrained) for English-Estonian and Estonian-English translation directions. The submitted systems were trained using Transformer models.
CUNI Transformer Neural MT System for WMT18
Authors: Martin Popel
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: coreference-resolution, Coreference Resolution, Machine Translation, NMT, Translation

We describe our NMT system submitted to the WMT2018 shared task in news translation. Our system is based on the Transformer model (Vaswani et al., 2017). We use an improved technique of backtranslation, where we iterate the process of translating monolingual data in one direction and training an NMT model for the opposite direction using synthetic parallel data. We apply a simple but effective filtering of the synthetic data. We pre-process the input sentences using coreference resolution in order to disambiguate the gender of pro-dropped personal pronouns. Finally, we apply two simple post-processing substitutions on the translated output. Our system is significantly (p {\textless} 0.05) better than all other English-Czech and Czech-English systems in WMT2018.
The University of Helsinki submissions to the WMT18 news task
Authors: Aless Raganato, ro, Yves Scherrer, Tommi Nieminen, Arvi Hurskainen, J{\"o}rg Tiedemann
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

This paper describes the University of Helsinki{'}s submissions to the WMT18 shared news translation task for English-Finnish and English-Estonian, in both directions. This year, our main submissions employ a novel neural architecture, the Transformer, using the open-source OpenNMT framework. Our experiments couple domain labeling and fine tuned multilingual models with shared vocabularies between the source and target language, using the provided parallel data of the shared task and additional back-translations. Finally, we compare, for the English-to-Finnish case, the effectiveness of different machine translation architectures, starting from a rule-based approach to our best neural model, analyzing the output and highlighting future research.
The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018
Authors: Julian Schamper, Jan Rosendahl, Parnia Bahar, Yunsu Kim, Arne Nix, Hermann Ney
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

This paper describes the statistical machine translation systems developed at RWTH Aachen University for the German→English, English→Turkish and Chinese→English translation tasks of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). We use ensembles of neural machine translation systems based on the Transformer architecture. Our main focus is on the German→English task where we to all automatic scored first with respect metrics provided by the organizers. We identify data selection, fine-tuning, batch size and model dimension as important hyperparameters. In total we improve by 6.8{\%} BLEU over our last year{'}s submission and by 4.8{\%} BLEU over the winning system of the 2017 German→English task. In English→Turkish task, we show 3.6{\%} BLEU improvement over the last year{'}s winning system. We further report results on the Chinese→English task where we improve 2.2{\%} BLEU on average over our baseline systems but stay behind the 2018 winning systems.
The LMU Munich Unsupervised Machine Translation Systems
Authors: Dario Stojanovski, Viktor Hangya, Matthias Huck, Alex Fraser, er
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Denoising, Language Modeling, Language Modelling, Machine Translation, Translation, Unsupervised Machine Translation, Word Embeddings

We describe LMU Munich{'}s unsupervised machine translation systems for Englishâ†”German translation. These systems were used to participate in the WMT18 news translation shared task and more specifically, for the unsupervised learning sub-track. The systems are trained on English and German monolingual data only and exploit and combine previously proposed techniques such as using word-by-word translated data based on bilingual word embeddings, denoising and on-the-fly backtranslation.
Tencent Neural Machine Translation Systems for WMT18
Authors: Mingxuan Wang, Li Gong, Wenhuan Zhu, Jun Xie, Chao Bian
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Data Augmentation, Machine Translation, Reranking, Translation

We participated in the WMT 2018 shared news translation task on English↔Chinese language pair. Our systems are based on attentional sequence-to-sequence models with some form of recursion and self-attention. Some data augmentation methods are also introduced to improve the translation performance. The best translation result is obtained with ensemble and reranking techniques. Our Chinese→English system achieved the highest cased BLEU score among all 16 submitted systems, and our English→Chinese system ranked the third out of 18 submitted systems.
The NiuTrans Machine Translation System for WMT18
Authors: Qiang Wang, Bei Li, Jiqiang Liu, Bojian Jiang, Zheyang Zhang, Yinqiao Li, Ye Lin, Tong Xiao, Jingbo Zhu
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Reranking, Translation

This paper describes the submission of the NiuTrans neural machine translation system for the WMT 2018 Chinese ↔ English news translation tasks. Our baseline systems are based on the Transformer architecture. We further improve the translation performance 2.4-2.6 BLEU points from four aspects, including architectural improvements, diverse ensemble decoding, reranking, and post-processing. Among constrained submissions, we rank 2nd out of 16 submitted systems on Chinese → English task and 3rd out of 16 on English → Chinese task, respectively.
The University of Maryland's Chinese-English Neural Machine Translation Systems at WMT18
Authors: Weijia Xu, Marine Carpuat
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Reranking, Translation

This paper describes the University of Maryland{'}s submission to the WMT 2018 Chinese↔English news translation tasks. Our systems are BPE-based self-attentional Transformer networks with parallel and backtranslated monolingual training data. Using ensembling and reranking, we improve over the Transformer baseline by +1.4 BLEU for Chinese→English and +3.97 BLEU for English→Chinese on \textit{newstest2017}. Our best systems reach BLEU scores of 24.4 for Chinese→English and 39.0 for English→Chinese on \textit{newstest2018}.
EvalD Reference-Less Discourse Evaluation for WMT18
Authors: Ond{\v{r}}ej Bojar, Ji{\v{r}}{\'\i} M{\'\i}rovsk{\'y}, Kate{\v{r}}ina Rysov{\'a}, Magdal{\'e}na Rysov{\'a}
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

We present the results of automatic evaluation of discourse in machine translation (MT) outputs using the EVALD tool. EVALD was originally designed and trained to assess the quality of \textit{human} writing, for native speakers and foreign-language learners. MT has seen a tremendous leap in translation quality at the level of sentences and it is thus interesting to see if the human-level evaluation is becoming relevant.