  Automatic translation of dialogue texts is a much needed demand in many real
life scenarios. However, the currently existing neural machine translation
delivers unsatisfying results. In this paper, we conduct a deep analysis of a
dialogue corpus and summarize three major issues on dialogue translation,
including pronoun dropping (\droppro), punctuation dropping (\droppun), and
typos (\typo). In response to these challenges, we propose a joint learning
method to identify omission and typo, 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%. We will publish the code and dataset publicly at
https://github.com/rgwt123/DialogueMT.
