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        <title>Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation</title>
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    <abstract>In encoder-decoder neural models, multiple encoders are in general used to represent the contextual information in addition to the individual sentence. In this paper, we investigate multi-encoder approaches in document-level neural machine translation (NMT). Surprisingly, we find that the context encoder does not only encode the surrounding sentences but also behaves as a noise generator. This makes us rethink the real benefits of multi-encoder in context-aware translation - some of the improvements come from robust training. We compare several methods that introduce noise and/or well-tuned dropout setup into the training of these encoders. Experimental results show that noisy training plays an important role in multi-encoder-based NMT, especially when the training data is small. Also, we establish a new state-of-the-art on IWSLT Fr-En task by careful use of noise generation and dropout methods.</abstract>
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%0 Conference Proceedings
%T Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation
%A Li, Bei
%A Liu, Hui
%A Wang, Ziyang
%A Jiang, Yufan
%A Xiao, Tong
%A Zhu, Jingbo
%A Liu, Tongran
%A Li, Changliang
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-multi-encoder
%X In encoder-decoder neural models, multiple encoders are in general used to represent the contextual information in addition to the individual sentence. In this paper, we investigate multi-encoder approaches in document-level neural machine translation (NMT). Surprisingly, we find that the context encoder does not only encode the surrounding sentences but also behaves as a noise generator. This makes us rethink the real benefits of multi-encoder in context-aware translation - some of the improvements come from robust training. We compare several methods that introduce noise and/or well-tuned dropout setup into the training of these encoders. Experimental results show that noisy training plays an important role in multi-encoder-based NMT, especially when the training data is small. Also, we establish a new state-of-the-art on IWSLT Fr-En task by careful use of noise generation and dropout methods.
%R 10.18653/v1/2020.acl-main.322
%U https://aclanthology.org/2020.acl-main.322
%U https://doi.org/10.18653/v1/2020.acl-main.322
%P 3512-3518

Markdown (Informal)

[Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation](https://aclanthology.org/2020.acl-main.322) (Li et al., ACL 2020)

• Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation (Li et al., ACL 2020)

ACL

• Bei Li, Hui Liu, Ziyang Wang, Yufan Jiang, Tong Xiao, Jingbo Zhu, Tongran Liu, and Changliang Li. 2020. Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3512–3518, Online. Association for Computational Linguistics.