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        <title>Adaptive Multi-pass Decoder for Neural Machine Translation</title>
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        <namePart type="given">Xinwei</namePart>
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    <abstract>Although end-to-end neural machine translation (NMT) has achieved remarkable progress in the recent years, the idea of adopting multi-pass decoding mechanism into conventional NMT is not well explored. In this paper, we propose a novel architecture called adaptive multi-pass decoder, which introduces a flexible multi-pass polishing mechanism to extend the capacity of NMT via reinforcement learning. More specifically, we adopt an extra policy network to automatically choose a suitable and effective number of decoding passes, according to the complexity of source sentences and the quality of the generated translations. Extensive experiments on Chinese-English translation demonstrate the effectiveness of our proposed adaptive multi-pass decoder upon the conventional NMT with a significant improvement about 1.55 BLEU.</abstract>
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%0 Conference Proceedings
%T Adaptive Multi-pass Decoder for Neural Machine Translation
%A Geng, Xinwei
%A Feng, Xiaocheng
%A Qin, Bing
%A Liu, Ting
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F geng-etal-2018-adaptive
%X Although end-to-end neural machine translation (NMT) has achieved remarkable progress in the recent years, the idea of adopting multi-pass decoding mechanism into conventional NMT is not well explored. In this paper, we propose a novel architecture called adaptive multi-pass decoder, which introduces a flexible multi-pass polishing mechanism to extend the capacity of NMT via reinforcement learning. More specifically, we adopt an extra policy network to automatically choose a suitable and effective number of decoding passes, according to the complexity of source sentences and the quality of the generated translations. Extensive experiments on Chinese-English translation demonstrate the effectiveness of our proposed adaptive multi-pass decoder upon the conventional NMT with a significant improvement about 1.55 BLEU.
%R 10.18653/v1/D18-1048
%U https://aclanthology.org/D18-1048/
%U https://doi.org/10.18653/v1/D18-1048
%P 523-532

Markdown (Informal)

[Adaptive Multi-pass Decoder for Neural Machine Translation](https://aclanthology.org/D18-1048/) (Geng et al., EMNLP 2018)

• Adaptive Multi-pass Decoder for Neural Machine Translation (Geng et al., EMNLP 2018)

ACL

• Xinwei Geng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2018. Adaptive Multi-pass Decoder for Neural Machine Translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 523–532, Brussels, Belgium. Association for Computational Linguistics.