Reinforced Target-driven Conversational Promotion

Huy Dao, Lizi Liao, Dung Le, Yuxiang Nie


Abstract
The ability to proactively engage with users towards pitching products is highly desired for conversational assistants. However, existing conversational recommendation methods overemphasize on acquiring user preferences while ignore the strategic planning for nudging users towards accepting a designated item. Hence, these methods fail to promote specified items with engaging responses. In this work, we propose a Reinforced Target-driven Conversational Promotion (RTCP) framework for conversational promotion. RTCP integrates short-term and long-term planning via a balanced gating mechanism. Inside which, the dialogue actions are predicted via a knowledge-integrated multi-head attention and guided via reinforcement learning rewards. RTCP then employs action-guided prefix tuning to generate relevant responses. Experimental results demonstrate that our model outperforms state-of-the-art models on both automatic metrics and human evaluation. Moreover, RTCP has a strong capability in quickly adapting to unseen scenarios just by updating prefix parameters without re-training the whole model.
Anthology ID:
2023.emnlp-main.775
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12583–12596
Language:
URL:
https://aclanthology.org/2023.emnlp-main.775
DOI:
10.18653/v1/2023.emnlp-main.775
Bibkey:
Cite (ACL):
Huy Dao, Lizi Liao, Dung Le, and Yuxiang Nie. 2023. Reinforced Target-driven Conversational Promotion. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12583–12596, Singapore. Association for Computational Linguistics.
Cite (Informal):
Reinforced Target-driven Conversational Promotion (Dao et al., EMNLP 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2023.emnlp-main.775.pdf