Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2023

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.

Keywords

Conversational recommendations, Dialogue strategy, Gating mechanisms, Knowledge integrated, Long term planning, Recommendation methods, Reinforcement learnings, Target driven, Tuning method

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

2023 Conference on Empirical Methods in Natural Language Processing: Singapore, December 6-10: Proceedings

First Page

12583

Last Page

12596

ISBN

9798891760608

Identifier

10.18653/v1/2023.emnlp-main.775

Publisher

Association for Computational Linguistics

City or Country

Stroudsburg, PA

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.18653/v1/2023.emnlp-main.775

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