中国邮电高校学报(英文)

中国邮电高校学报(英文) ›› 2021, Vol. 28 ›› Issue (1): 1-9.doi: 10.19682/j.cnki.1005-8885.2021.0005

• Artificial Intelligence •    下一篇

Knowledge-aware path: interpretable graph reasoning in proactive dialogue generation

孙忆南,徐雅静,李思,郭军   

  1. 北京邮电大学
  • 收稿日期:2020-03-28 修回日期:2020-08-09 出版日期:2021-02-28 发布日期:2021-03-28
  • 通讯作者: 徐雅静 E-mail:xyj@bupt.edu.cn
  • 基金资助:
    This work was supported by the National Natural Science Foundation of China (61702047)

Knowledge-aware path: interpretable graph reasoning in proactive dialogue generation

  1. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2020-03-28 Revised:2020-08-09 Online:2021-02-28 Published:2021-03-28
  • Contact: Ya-Jing XU E-mail:xyj@bupt.edu.cn
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61702047)

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摘要：

Proactive dialogue generates dialogue utterance based on a conversation goal and a given knowledge graph (KG). Existing methods combine knowledge of each turn of dialogue with knowledge triples by hidden variables, resulting in the interpretability of generation results is relatively poor. An interpretable knowledge-aware path (KAP) model was proposed for knowledge reasoning in proactive dialogue generation. KAP model can transform explicit and implicit knowledge of each turn of dialogue into corresponding dialogue state matrix, thus forming the KAP for dialogue history. Based on KAP, the next turn of dialogue state vector can be infered from both the topology and semantic of KG. This vector can indicate knowledge distribution of next sentence, so it enhances the accuracy and interpretability of dialogue generation. Experiments show that KAP  model’s dialogue generation is closer to actual conversation than other state-of-the-art proactive dialogue models.


关键词: knowledge-aware path|graph reasoning|proactive dialogue system

Abstract:

Proactive dialogue generates dialogue utterance based on a conversation goal and a given knowledge graph (KG). Existing methods combine knowledge of each turn of dialogue with knowledge triples by hidden variables, resulting in the interpretability of generation results is relatively poor. An interpretable knowledge-aware path (KAP) model was proposed for knowledge reasoning in proactive dialogue generation. KAP model can transform explicit and implicit knowledge of each turn of dialogue into corresponding dialogue state matrix, thus forming the KAP for dialogue history. Based on KAP, the next turn of dialogue state vector can be infered from both the topology and semantic of KG. This vector can indicate knowledge distribution of next sentence, so it enhances the accuracy and interpretability of dialogue generation. Experiments show that KAP  model’s dialogue generation is closer to actual conversation than other state-of-the-art proactive dialogue models.

Key words: knowledge-aware path|graph reasoning|proactive dialogue system

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