Neural Machine Translation Based on Improved Actor-Critic Method

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.