关键词: 基于文档的对话, 回复生成, 信息筛选, Transformer模型, 注意力机制

Abstract: Document grounded conversations is an emerging hot task in the field of dialogue system. Different from previous tasks, it needs to consider both the utterances and the given document. However, previous work focused on the relationship between the two, but ignored the utterances’ difference in the effect of response generation. To solve this problem, a new dialectical approach to the dialogue history, which means the utterances before the last one, is proposed in this paper. At the encoding step, it divides the modeling of the semantic information into two parts: using history and ignoring history, and then uses the comparative integration method to summarize the branch results. In this way, when the dialogue history is not related to the current utterance, it can avoid being introduced as noise which will damage the performance of the model. Besides, it also strengthens the guiding role of the current utterance in the information filtering process. Experimental results show that compared with the existing baselines, this model can generate responses that are more in line with the current context and more informative, indicating that it can better understand dialogue information and conduct knowledge filtering. And through the ablation study, the effectiveness of each module in the modeling process is also verified.

Key words: document grounded conversations, reply generation, information filtering, Transformer model, attention mechanism