TY - GEN
T1 - KERS
T2 - 2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021
AU - Zhang, Jun
AU - Yang, Yan
AU - Chen, Chengcai
AU - He, Liang
AU - Yu, Zhou
N1 - Publisher Copyright:
© 2021 Association for Computational Linguistics.
PY - 2021
Y1 - 2021
N2 - Recommendation dialogs require the system to build a social bond with users to gain trust and develop affinity in order to increase the chance of a successful recommendation. It is beneficial to divide up, such conversations with multiple subgoals (such as social chat, question answering, recommendation, etc.), so that the system can retrieve appropriate knowledge with better accuracy under different subgoals. In this paper, we propose a unified framework for common knowledge-based multi-subgoal dialog: knowledge-enhanced multi-subgoal driven recommender system (KERS). We first predict a sequence of subgoals and use them to guide the dialog model to select knowledge from a sub-set of existing knowledge graph. We then propose three new mechanisms to filter noisy knowledge and to enhance the inclusion of cleaned knowledge in the dialog response generation process. Experiments show that our method obtains stateof-the-art results on DuRecDial dataset in both automatic and human evaluation.
AB - Recommendation dialogs require the system to build a social bond with users to gain trust and develop affinity in order to increase the chance of a successful recommendation. It is beneficial to divide up, such conversations with multiple subgoals (such as social chat, question answering, recommendation, etc.), so that the system can retrieve appropriate knowledge with better accuracy under different subgoals. In this paper, we propose a unified framework for common knowledge-based multi-subgoal dialog: knowledge-enhanced multi-subgoal driven recommender system (KERS). We first predict a sequence of subgoals and use them to guide the dialog model to select knowledge from a sub-set of existing knowledge graph. We then propose three new mechanisms to filter noisy knowledge and to enhance the inclusion of cleaned knowledge in the dialog response generation process. Experiments show that our method obtains stateof-the-art results on DuRecDial dataset in both automatic and human evaluation.
UR - https://www.scopus.com/pages/publications/85129147419
U2 - 10.18653/v1/2021.findings-emnlp.94
DO - 10.18653/v1/2021.findings-emnlp.94
M3 - 会议稿件
AN - SCOPUS:85129147419
T3 - Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021
SP - 1092
EP - 1101
BT - Findings of the Association for Computational Linguistics, Findings of ACL
A2 - Moens, Marie-Francine
A2 - Huang, Xuanjing
A2 - Specia, Lucia
A2 - Yih, Scott Wen-Tau
PB - Association for Computational Linguistics (ACL)
Y2 - 7 November 2021 through 11 November 2021
ER -