Integrating Reconstructor and Post-Editor into Neural Machine Translation

Abstract

Neural machine translation (NMT) mainly comprises the encoder and decoder. The encoder is mainly used to extract the feature vector of the source language sentence. The decoder predicts the next token according to the feature vector extracted by the encoder and the information of the current moment. In this process, there is no guarantee that the features extracted by the encoder are indistinguishable from the meaning of the sentences in the source language. There is also no guarantee that the decoder can accurately predict the corresponding character. These issues can lead to over-translation and under-translation issues in the translated results. Previous researchers alleviated this problem by calculating the gap between the reconstructed source-language sentences and the source-language sentences. Inspired by this method, we propose to integrate a reconstructor and a post-editor into NMT during the training. The reconstructor takes the translation of NMT as input to reconstruct the source sentence, and the post-editor takes the translation as input and post-edits it to predict the target sentence. Through the training of the reconstructor and the post-editor, the semantics of the translation are forced to follow the source sentence and the target sentence. Experimental results show that our approach can effectively improve the performance of NMT on multiple translation tasks.