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        <namePart type="given">Yongchao</namePart>
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        <namePart type="given">Hongfei</namePart>
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    <name type="personal">
        <namePart type="given">Xiangyu</namePart>
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    <name type="personal">
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    <abstract>Multi-Domain Neural Machine Translation (NMT) aims at building a single system that performs well on a range of target domains. However, along with the extreme diversity of cross-domain wording and phrasing style, the imperfections of training data distribution and the inherent defects of the current sequential learning process all contribute to making the task of multi-domain NMT very challenging. To mitigate these problems, we propose the Factorized Transformer, which consists of an in-depth factorization of the parameters of an NMT model, namely Transformer in this paper, into two categories: domain-shared ones that encode common cross-domain knowledge and domain-specific ones that are private for each constituent domain. We experiment with various designs of our model and conduct extensive validations on English to French open multi-domain dataset. Our approach achieves state-of-the-art performance and opens up new perspectives for multi-domain and open-domain applications.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.377</identifier>
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%0 Conference Proceedings
%T Factorized Transformer for Multi-Domain Neural Machine Translation
%A Deng, Yongchao
%A Yu, Hongfei
%A Yu, Heng
%A Duan, Xiangyu
%A Luo, Weihua
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F deng-etal-2020-factorized
%X Multi-Domain Neural Machine Translation (NMT) aims at building a single system that performs well on a range of target domains. However, along with the extreme diversity of cross-domain wording and phrasing style, the imperfections of training data distribution and the inherent defects of the current sequential learning process all contribute to making the task of multi-domain NMT very challenging. To mitigate these problems, we propose the Factorized Transformer, which consists of an in-depth factorization of the parameters of an NMT model, namely Transformer in this paper, into two categories: domain-shared ones that encode common cross-domain knowledge and domain-specific ones that are private for each constituent domain. We experiment with various designs of our model and conduct extensive validations on English to French open multi-domain dataset. Our approach achieves state-of-the-art performance and opens up new perspectives for multi-domain and open-domain applications.
%R 10.18653/v1/2020.findings-emnlp.377
%U https://aclanthology.org/2020.findings-emnlp.377
%U https://doi.org/10.18653/v1/2020.findings-emnlp.377
%P 4221-4230

Markdown (Informal)

[Factorized Transformer for Multi-Domain Neural Machine Translation](https://aclanthology.org/2020.findings-emnlp.377) (Deng et al., Findings 2020)

• Factorized Transformer for Multi-Domain Neural Machine Translation (Deng et al., Findings 2020)

ACL

• Yongchao Deng, Hongfei Yu, Heng Yu, Xiangyu Duan, and Weihua Luo. 2020. Factorized Transformer for Multi-Domain Neural Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4221–4230, Online. Association for Computational Linguistics.