pwc-archive/papers-with-abstracts huggingface dataset:
DBMS-KU Interpolation for WMT19 News Translation Task
Authors: Sari Dewi Budiwati, Al Hafiz Akbar Maulana Siagian, Tirana Noor Fatyanosa, Masayoshi Aritsugi
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Language Modeling, Language Modelling, Translation

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.
The TALP-UPC Machine Translation Systems for WMT19 News Translation Task: Pivoting Techniques for Low Resource MT
Authors: Noe Casas, Jos{\'e} A. R. Fonollosa, Carlos Escolano, Christine Basta, Marta R. Costa-juss{\`a}
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Decoder, Machine Translation, Translation

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
Authors: Fabien Cromieres, Sadao Kurohashi
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation

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 {``}checkpoint agreement{''}.
NICT's Supervised Neural Machine Translation Systems for the WMT19 News Translation Task
Authors: Raj Dabre, Kehai Chen, Benjamin Marie, Rui Wang, Atsushi Fujita, Masao Utiyama, Eiichiro Sumita
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, NMT, Transfer Learning, Translation

In this paper, we describe our supervised neural machine translation (NMT) systems that we developed for the news translation task for Kazakh↔English, Gujarati↔English, Chinese↔English, and English→Finnish translation directions. We focused on leveraging multilingual transfer learning and back-translation for the extremely low-resource language pairs: Kazakh↔English and Gujarati↔English translation. For the Chinese↔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↔English. For English→Finnish, our submission from WMT18 remains a strong baseline despite the increase in parallel corpora for this year{'}s task.
UdS-DFKI Participation at WMT 2019: Low-Resource (en-gu) and Coreference-Aware (en-de) Systems
Authors: Cristina Espa{\~n}a-Bonet, Dana Ruiter
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Translation

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
Authors: Vikrant Goyal, Dipti Misra Sharma
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Decoder, Machine Translation, Translation

This paper describes the Neural Machine Translation system of IIIT-Hyderabad for the Gujaratiâ†’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
Authors: Xinze Guo, Chang Liu, Xiaolong Li, Yiran Wang, Guoliang Li, Feng Wang, Zhitao Xu, Liuyi Yang, Li Ma, Changliang Li
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Reranking, Translation

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
Authors: Jeremy Gwinnup, Grant Erdmann, Tim Anderson
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Domain Adaptation, Machine Translation, Translation

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.
The MLLP-UPV Supervised Machine Translation Systems for WMT19 News Translation Task
Authors: Javier Iranzo-S{\'a}nchez, Gon{\c{c}}al Garc{\'e}s D{\'\i}az-Mun{\'\i}o, Jorge Civera, Alfons Juan
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Domain Adaptation, Machine Translation, Translation

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 ↔ English and German ↔ 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.
CUNI Submission for Low-Resource Languages in WMT News 2019
Authors: Tom Kocmi, Ond{\v{r}}ej Bojar
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Transfer Learning, Translation

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
Authors: Ivana Kvapil{\'\i}kov{\'a}, Dominik Mach{\'a}{\v{c}}ek, Ond{\v{r}}ej Bojar
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation

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
Authors: Zhenhao Li, Lucia Specia
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Translation

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
Authors: Bei Li, Yinqiao Li, Chen Xu, Ye Lin, Jiqiang Liu, Hui Liu, Ziyang Wang, Yuhao Zhang, Nuo Xu, Zeyang Wang, Kai Feng, Hexuan Chen, Tengbo Liu, Yanyang Li, Qiang Wang, Tong Xiao, Jingbo Zhu
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Knowledge Distillation, Machine Translation, NMT, Reranking, Translation

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↔{ZH, DE, RU, KK, LT}, GU→EN and the unsupervised DE↔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↔EN, GU→EN} directions, ranking 2nd in {RU→EN, DE↔CS} and 3rd in {ZH→EN, LT→EN, EN→RU, EN↔DE} among all constrained submissions.
Multi-Source Transformer for Kazakh-Russian-English Neural Machine Translation
Authors: Patrick Littell, Chi-kiu Lo, Samuel Larkin, Darlene Stewart
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, NMT, Sentence, Translation

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.
Johns Hopkins University Submission for WMT News Translation Task
Authors: Kelly Marchisio, Yash Kumar Lal, Philipp Koehn
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: de-en, Machine Translation, Translation

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
Authors: Benjamin Marie, Haipeng Sun, Rui Wang, Kehai Chen, Atsushi Fujita, Masao Utiyama, Eiichiro Sumita
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation, Unsupervised Machine Translation

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 ({``}constraint{'}{''}), 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
Authors: Alex Molchanov, er
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Translation

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
Authors: Riktim Mondal, Shankha Raj Nayek, Aditya Chowdhury, Santanu Pal, Sudip Kumar Naskar, Josef van Genabith
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, NMT, Translation

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.
eTranslation's Submissions to the WMT 2019 News Translation Task
Authors: Csaba Oravecz, Katina Bontcheva, Adrien Lardilleux, L{\'a}szl{\'o} Tihanyi, Andreas Eisele
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Translation

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
Authors: Marcis Pinnis, Rihards Kri{\v{s}}lauks, Mat{\=\i}ss Rikters
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, NMT, Translation

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
Authors: Tommi Pirinen
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, test driven development, Translation

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.
The RWTH Aachen University Machine Translation Systems for WMT 2019
Authors: Jan Rosendahl, Christian Herold, Yunsu Kim, Miguel Gra{\c{c}}a, Weiyue Wang, Parnia Bahar, Yingbo Gao, Hermann Ney
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Attribute, de-en, Language Modeling, Language Modelling, Machine Translation, Transfer Learning, Translation

This paper describes the neural machine translation systems developed at the RWTH Aachen University for the German-English, Chinese-English and Kazakh-English news translation tasks of the Fourth Conference on Machine Translation (WMT19). For all tasks, the final submitted system is based on the Transformer architecture. We focus on improving data filtering and fine-tuning as well as systematically evaluating interesting approaches like unigram language model segmentation and transfer learning. For the De-En task, none of the tested methods gave a significant improvement over last years winning system and we end up with the same performance, resulting in 39.6{\%} BLEU on newstest2019. In the Zh-En task, we show 1.3{\%} BLEU improvement over our last year{'}s submission, which we mostly attribute to the splitting of long sentences during translation. We further report results on the Kazakh-English task where we gain improvements of 11.1{\%} BLEU over our baseline system. On the same task we present a recent transfer learning approach, which uses half of the free parameters of our submission system and performs on par with it.
The Universitat d'Alacant Submissions to the English-to-Kazakh News Translation Task at WMT 2019
Authors: V{\'\i}ctor M. S{\'a}nchez-Cartagena, Juan Antonio P{\'e}rez-Ortiz, Felipe S{\'a}nchez-Mart{\'\i}nez
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Transfer Learning, Translation

This paper describes the two submissions of Universitat d{'}Alacant to the English-to-Kazakh news translation task at WMT 2019. Our submissions take advantage of monolingual data and parallel data from other language pairs by means of iterative backtranslation, pivot backtranslation and transfer learning. They also use linguistic information in two ways: morphological segmentation of Kazakh text, and integration of the output of a rule-based machine translation system. Our systems were ranked second in terms of chrF++ despite being built from an ensemble of only 2 independent training runs.
CUED@WMT19:EWC\&LMs
Authors: Felix Stahlberg, Danielle Saunders, Adri{\`a} de Gispert, Bill Byrne
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Language Modelling, Sentence

Two techniques provide the fabric of the Cambridge University Engineering Department{'}s (CUED) entry to the WMT19 evaluation campaign: elastic weight consolidation (EWC) and different forms of language modelling (LMs). We report substantial gains by fine-tuning very strong baselines on former WMT test sets using a combination of checkpoint averaging and EWC. A sentence-level Transformer LM and a document-level LM based on a modified Transformer architecture yield further gains. As in previous years, we also extract n-gram probabilities from SMT lattices which can be seen as a source-conditioned n-gram LM.
Baidu Neural Machine Translation Systems for WMT19
Authors: Meng Sun, Bojian Jiang, Hao Xiong, Zhongjun He, Hua Wu, Haifeng Wang
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Data Augmentation, Domain Adaptation, Knowledge Distillation, Machine Translation, Re-Ranking, Translation

In this paper we introduce the systems Baidu submitted for the WMT19 shared task on Chinese{\textless}-{\textgreater}English news translation. Our systems are based on the Transformer architecture with some effective improvements. Data selection, back translation, data augmentation, knowledge distillation, domain adaptation, model ensemble and re-ranking are employed and proven effective in our experiments. Our Chinese-{\textgreater}English system achieved the highest case-sensitive BLEU score among all constrained submissions, and our English-{\textgreater}Chinese system ranked the second in all submissions.
University of Tartu's Multilingual Multi-domain WMT19 News Translation Shared Task Submission
Authors: Andre T{\"a}ttar, Elizaveta Korotkova, Mark Fishel
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Translation

This paper describes the University of Tartu{'}s submission to the news translation shared task of WMT19, where the core idea was to train a single multilingual system to cover several language pairs of the shared task and submit its results. We only used the constrained data from the shared task. We describe our approach and its results and discuss the technical issues we faced.
Neural Machine Translation for English--Kazakh with Morphological Segmentation and Synthetic Data
Authors: Antonio Toral, Lukas Edman, Galiya Yeshmagambetova, Jennifer Spenader
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation

This paper presents the systems submitted by the University of Groningen to the English{--} Kazakh language pair (both translation directions) for the WMT 2019 news translation task. We explore the potential benefits of (i) morphological segmentation (both unsupervised and rule-based), given the agglutinative nature of Kazakh, (ii) data from two additional languages (Turkish and Russian), given the scarcity of English{--}Kazakh data and (iii) synthetic data, both for the source and for the target language. Our best submissions ranked second for Kazakh→English and third for English→Kazakh in terms of the BLEU automatic evaluation metric.