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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.06769 |
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| _version_ | 1866914954107420672 |
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| author | Zhang, Tong Huang, Chen Deng, Yang Liang, Hongru Liu, Jia Wen, Zujie Lei, Wenqiang Chua, Tat-Seng |
| author_facet | Zhang, Tong Huang, Chen Deng, Yang Liang, Hongru Liu, Jia Wen, Zujie Lei, Wenqiang Chua, Tat-Seng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06769 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation Zhang, Tong Huang, Chen Deng, Yang Liang, Hongru Liu, Jia Wen, Zujie Lei, Wenqiang Chua, Tat-Seng Computation and Language 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. |
| title | Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.06769 |