@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",
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.",
}
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<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>
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%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
%S Proceedings of the Third Conference on Machine Translation: Shared Task Papers
%D 2018
%8 oct
%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., 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.