Lei Li, Tao Wang, Deyi Xiong, Mingxuan Wang, Chengqi Zhao
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Automatic translation of dialogue texts is a much needed demand in many real life scenarios. However, current neural machine translation systems usually deliver unsatisfying translation results of dialogue texts. In this paper, we conduct a deep analysis of a dialogue corpus and summarize three major issues on dialogue translation, including pronoun dropping (ProDrop), punctuation dropping (PunDrop), and typos (DialTypo). In response to these challenges, we propose a joint learning method to identify omission and typo in the process of translating, and utilize context to translate dialogue utterances. To properly evaluate the performance, we propose a manually annotated dataset with 1,931 Chinese-English parallel utterances from 300 dialogues as a benchmark testbed for dialogue translation. Our experiments show that the proposed method improves translation quality by 3.2 BLEU over the baselines. It also elevates the recovery rate of omitted pronouns from 26.09% to 47.16%. The code and dataset are publicly available at https://github.com/rgwt123/DialogueMT.
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