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2020 | OriginalPaper | Buchkapitel

Neural Machine Translation Based on Improved Actor-Critic Method

verfasst von : Ziyue Guo, Hongxu Hou, Nier Wu, Shuo Sun

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2020

Verlag: Springer International Publishing

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Abstract

Reinforcement learning based neural machine translation (NMT) is limited by the sparse reward problem which further affects the quality of the model, and the actor-critic method is mainly used to enrich the reward of the output fragments. But for low-resource agglutinative languages, it does not show significant results. To this end, we propose an novel actor-critic approach that provides additional affix-level rewards and also combines the traditional token-level rewards to guide the parameters update of the NMT model. In addition, for purpose of improving the decoding speed, we utilize an improved non-autoregressive model as the actor model to make it pay more attention to the translation quality while outputting in parallel. We achieve remarkable progress on two translation tasks, including the low-resource Mongolian-Chinese and the public NIST English-Chinese, while significantly shorting training time and accomplishing faster convergence.

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Vorheriges Kapitel Guided Reinforcement Learning via Sequence Learning

Nächstes Kapitel Neural Machine Translation Based on Prioritized Experience Replay

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Titel: Neural Machine Translation Based on Improved Actor-Critic Method
verfasst von: Ziyue Guo
Hongxu Hou
Nier Wu
Shuo Sun
Verlag: Springer International Publishing
Buch: Artificial Neural Networks and Machine Learning – ICANN 2020
Print ISBN: 978-3-030-61615-1

Electronic ISBN: 978-3-030-61616-8

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-61616-8_28

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