Alibaba’s Neural Machine Translation Systems for WMT18
Yongchao Deng, Shanbo Cheng, Jun Lu, Kai Song, Jingang Wang, Shenglan Wu, Liang Yao, Guchun Zhang, Haibo Zhang, Pei Zhang, Changfeng Zhu, Boxing Chen
Abstract
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.- Anthology ID:
- W18-6408
- Volume:
- Proceedings of the Third Conference on Machine Translation: Shared Task Papers
- Month:
- October
- Year:
- 2018
- Address:
- Belgium, Brussels
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Lucia Specia, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 368–376
- Language:
- URL:
- https://aclanthology.org/W18-6408
- DOI:
- 10.18653/v1/W18-6408
- Bibkey:
- Cite (ACL):
- Yongchao Deng, Shanbo Cheng, Jun Lu, Kai Song, Jingang Wang, Shenglan Wu, Liang Yao, Guchun Zhang, Haibo Zhang, Pei Zhang, Changfeng Zhu, and Boxing Chen. 2018. Alibaba’s Neural Machine Translation Systems for WMT18. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 368–376, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- Alibaba’s Neural Machine Translation Systems for WMT18 (Deng et al., WMT 2018)
- Copy Citation:
- PDF:
- https://aclanthology.org/W18-6408.pdf
Export citation
@inproceedings{deng-etal-2018-alibabas, title = "{A}libaba{'}s Neural Machine Translation Systems for {WMT}18", author = "Deng, Yongchao and Cheng, Shanbo and Lu, Jun and Song, Kai and Wang, Jingang and Wu, Shenglan and Yao, Liang and Zhang, Guchun and Zhang, Haibo and Zhang, Pei and Zhu, Changfeng and Chen, Boxing", editor = "Bojar, Ond{\v{r}}ej and Chatterjee, Rajen and Federmann, Christian and Fishel, Mark and Graham, Yvette and Haddow, Barry and Huck, Matthias and Yepes, Antonio Jimeno and Koehn, Philipp and Monz, Christof and Negri, Matteo and N{\'e}v{\'e}ol, Aur{\'e}lie and Neves, Mariana and Post, Matt and Specia, Lucia and Turchi, Marco and Verspoor, Karin", booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers", month = oct, year = "2018", address = "Belgium, Brussels", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W18-6408", doi = "10.18653/v1/W18-6408", pages = "368--376", abstract = "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.", }
<?xml version="1.0" encoding="UTF-8"?> <modsCollection xmlns="http://www.loc.gov/mods/v3"> <mods ID="deng-etal-2018-alibabas"> <titleInfo> <title>Alibaba’s Neural Machine Translation Systems for WMT18</title> </titleInfo> <name type="personal"> <namePart type="given">Yongchao</namePart> <namePart type="family">Deng</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Shanbo</namePart> <namePart type="family">Cheng</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Jun</namePart> <namePart type="family">Lu</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Kai</namePart> <namePart type="family">Song</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Jingang</namePart> <namePart type="family">Wang</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Shenglan</namePart> <namePart type="family">Wu</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Liang</namePart> <namePart type="family">Yao</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Guchun</namePart> <namePart type="family">Zhang</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Haibo</namePart> <namePart type="family">Zhang</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Pei</namePart> <namePart type="family">Zhang</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Changfeng</namePart> <namePart type="family">Zhu</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Boxing</namePart> <namePart type="family">Chen</namePart> <role> <roleTerm authority="marcrelator" type="text">author</roleTerm> </role> </name> <originInfo> <dateIssued>2018-10</dateIssued> </originInfo> <typeOfResource>text</typeOfResource> <relatedItem type="host"> <titleInfo> <title>Proceedings of the Third Conference on Machine Translation: Shared Task Papers</title> </titleInfo> <name type="personal"> <namePart type="given">Ondřej</namePart> <namePart type="family">Bojar</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Rajen</namePart> <namePart type="family">Chatterjee</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Christian</namePart> <namePart type="family">Federmann</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Mark</namePart> <namePart type="family">Fishel</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Yvette</namePart> <namePart type="family">Graham</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Barry</namePart> <namePart type="family">Haddow</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Matthias</namePart> <namePart type="family">Huck</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Antonio</namePart> <namePart type="given">Jimeno</namePart> <namePart type="family">Yepes</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Philipp</namePart> <namePart type="family">Koehn</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Christof</namePart> <namePart type="family">Monz</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Matteo</namePart> <namePart type="family">Negri</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Aurélie</namePart> <namePart type="family">Névéol</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Mariana</namePart> <namePart type="family">Neves</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Matt</namePart> <namePart type="family">Post</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Lucia</namePart> <namePart type="family">Specia</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Marco</namePart> <namePart type="family">Turchi</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <name type="personal"> <namePart type="given">Karin</namePart> <namePart type="family">Verspoor</namePart> <role> <roleTerm authority="marcrelator" type="text">editor</roleTerm> </role> </name> <originInfo> <publisher>Association for Computational Linguistics</publisher> <place> <placeTerm type="text">Belgium, Brussels</placeTerm> </place> </originInfo> <genre authority="marcgt">conference publication</genre> </relatedItem> <abstract>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.</abstract> <identifier type="citekey">deng-etal-2018-alibabas</identifier> <identifier type="doi">10.18653/v1/W18-6408</identifier> <location> <url>https://aclanthology.org/W18-6408</url> </location> <part> <date>2018-10</date> <extent unit="page"> <start>368</start> <end>376</end> </extent> </part> </mods> </modsCollection>
%0 Conference Proceedings %T Alibaba’s Neural Machine Translation Systems for WMT18 %A Deng, Yongchao %A Cheng, Shanbo %A Lu, Jun %A Song, Kai %A Wang, Jingang %A Wu, Shenglan %A Yao, Liang %A Zhang, Guchun %A Zhang, Haibo %A Zhang, Pei %A Zhu, Changfeng %A Chen, Boxing %Y Bojar, Ondřej %Y Chatterjee, Rajen %Y Federmann, Christian %Y Fishel, Mark %Y Graham, Yvette %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Monz, Christof %Y Negri, Matteo %Y Névéol, Aurélie %Y Neves, Mariana %Y Post, Matt %Y Specia, Lucia %Y Turchi, Marco %Y Verspoor, Karin %S Proceedings of the Third Conference on Machine Translation: Shared Task Papers %D 2018 %8 October %I Association for Computational Linguistics %C Belgium, Brussels %F deng-etal-2018-alibabas %X 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. %R 10.18653/v1/W18-6408 %U https://aclanthology.org/W18-6408 %U https://doi.org/10.18653/v1/W18-6408 %P 368-376
Markdown (Informal)
[Alibaba’s Neural Machine Translation Systems for WMT18](https://aclanthology.org/W18-6408) (Deng et al., WMT 2018)
- Alibaba’s Neural Machine Translation Systems for WMT18 (Deng et al., WMT 2018)
ACL
- Yongchao Deng, Shanbo Cheng, Jun Lu, Kai Song, Jingang Wang, Shenglan Wu, Liang Yao, Guchun Zhang, Haibo Zhang, Pei Zhang, Changfeng Zhu, and Boxing Chen. 2018. Alibaba’s Neural Machine Translation Systems for WMT18. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 368–376, Belgium, Brussels. Association for Computational Linguistics.