Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages. Despite the promising performance of sentence-level and context-aware neural machine translation models, there still remain limitations in current NCT models because the inherent dialogue characteristics of chat, such as dialogue coherence and speaker personality, are neglected. In this paper, we propose to promote the chat translation by introducing the modeling of dialogue characteristics into the NCT model. To this end, we design four auxiliary tasks including monolingual response generation, cross-lingual response generation, next utterance discrimination, and speaker identification. Together with the main chat translation task, we optimize the NCT model through the training objectives of all these tasks. By this means, the NCT model can be enhanced by capturing the inherent dialogue characteristics, thus generating more coherent and speaker-relevant translations. Comprehensive experiments on four language directions (English-German and English-Chinese) verify the effectiveness and superiority of the proposed approach.

本文针对当前在聊天翻译模型中存在的限制提出了一种新的方法——将对话特征建模引入到模型中。为此，设计了四个辅助任务，包括同语种响应生成、跨语种响应生成、下一个话语辨别和说话人识别。综合实验结果显示，这种方法实现了更多一致且更有说服力的翻译。

利用对话特征的最新神经聊天翻译技术