Adaptive Multi-pass Decoder for Neural Machine Translation

Xinwei Geng, Xiaocheng Feng, Bing Qin, Ting Liu


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.
Anthology ID:
D18-1048
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
523–532
Language:
URL:
https://aclanthology.org/D18-1048
DOI:
10.18653/v1/D18-1048
Bibkey:
Cite (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.
Cite (Informal):
Adaptive Multi-pass Decoder for Neural Machine Translation (Geng et al., EMNLP 2018)
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PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/D18-1048.pdf