@inproceedings{zhang-etal-2024-strength,
title = "Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation",
author = "Zhang, Tong and
Huang, Chen and
Deng, Yang and
Liang, Hongru and
Liu, Jia and
Wen, Zujie and
Lei, Wenqiang and
Chua, Tat-Seng",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.26",
pages = "424--444",
abstract = "We investigate non-collaborative dialogue agents, which are expected to engage in strategic conversations with diverse users, for securing a mutual agreement that leans favorably towards the system{'}s objectives. This poses two main challenges for existing dialogue agents: 1) The inability to integrate user-specific characteristics into the strategic planning, and 2) The difficulty of training strategic planners that can be generalized to diverse users. To address these challenges, we propose TRIP to enhance the capability in tailored strategic planning, incorporating a user-aware strategic planning module and a population-based training paradigm. Through experiments on benchmark non-collaborative dialogue tasks, we demonstrate the effectiveness of TRIP in catering to diverse users.",
}
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%0 Conference Proceedings
%T Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation
%A Zhang, Tong
%A Huang, Chen
%A Deng, Yang
%A Liang, Hongru
%A Liu, Jia
%A Wen, Zujie
%A Lei, Wenqiang
%A Chua, Tat-Seng
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhang-etal-2024-strength
%X We investigate non-collaborative dialogue agents, which are expected to engage in strategic conversations with diverse users, for securing a mutual agreement that leans favorably towards the system’s objectives. This poses two main challenges for existing dialogue agents: 1) The inability to integrate user-specific characteristics into the strategic planning, and 2) The difficulty of training strategic planners that can be generalized to diverse users. To address these challenges, we propose TRIP to enhance the capability in tailored strategic planning, incorporating a user-aware strategic planning module and a population-based training paradigm. Through experiments on benchmark non-collaborative dialogue tasks, we demonstrate the effectiveness of TRIP in catering to diverse users.
%U https://aclanthology.org/2024.emnlp-main.26
%P 424-444
Markdown (Informal)
[Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation](https://aclanthology.org/2024.emnlp-main.26) (Zhang et al., EMNLP 2024)
ACL
- Tong Zhang, Chen Huang, Yang Deng, Hongru Liang, Jia Liu, Zujie Wen, Wenqiang Lei, and Tat-Seng Chua. 2024. Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 424–444, Miami, Florida, USA. Association for Computational Linguistics.