ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (9): 1915-1924.doi: 10.7544/issn1000-1239.2021.20200634

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Research on Document Grounded Conversations

Sun Runxin, Ma Longxuan, Zhang Weinan, Liu Ting   

  1. (Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, Harbin 150001)
  • Online:2021-09-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (62076081, 61772153, 61936010) and 2030 Major Project of New Generation Artificial Intelligence of China (2020AAA0108605).

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

CLC Number: