@inproceedings{jin-etal-2023-joint,
title = "Joint Semantic and Strategy Matching for Persuasive Dialogue",
author = "Jin, Chuhao and
Zhu, Yutao and
Kong, Lingzhen and
Li, Shijie and
Zhang, Xiao and
Song, Ruihua and
Chen, Xu and
Chen, Huan and
Sun, Yuchong and
Chen, Yu and
Xu, Jun",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.276",
doi = "10.18653/v1/2023.findings-emnlp.276",
pages = "4187--4197",
abstract = "Persuasive dialogue aims to persuade users to achieve some targets by conversations. While previous persuasion models have achieved notable successes, they mostly base themselves on utterance semantic matching, and an important aspect has been ignored, that is, the strategy of the conversations, for example, the agent can choose an \textit{emotional-appeal} strategy to impress users. Compared with utterance semantics, conversation strategies are high-level concepts, which can be informative and provide complementary information to achieve effective persuasions. In this paper, we propose to build a persuasion model by jointly modeling the conversation semantics and strategies, where we design a BERT-like module and an auto-regressive predictor to match the semantics and strategies, respectively. Experimental results indicate that our proposed approach can significantly improve the state-of-the-art baseline by 5{\%} on a small dataset and 37{\%} on a large dataset in terms of Recall@1. Detailed analyses show that the auto-regressive predictor contributes most to the final performance.",
}
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<abstract>Persuasive dialogue aims to persuade users to achieve some targets by conversations. While previous persuasion models have achieved notable successes, they mostly base themselves on utterance semantic matching, and an important aspect has been ignored, that is, the strategy of the conversations, for example, the agent can choose an emotional-appeal strategy to impress users. Compared with utterance semantics, conversation strategies are high-level concepts, which can be informative and provide complementary information to achieve effective persuasions. In this paper, we propose to build a persuasion model by jointly modeling the conversation semantics and strategies, where we design a BERT-like module and an auto-regressive predictor to match the semantics and strategies, respectively. Experimental results indicate that our proposed approach can significantly improve the state-of-the-art baseline by 5% on a small dataset and 37% on a large dataset in terms of Recall@1. Detailed analyses show that the auto-regressive predictor contributes most to the final performance.</abstract>
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%0 Conference Proceedings
%T Joint Semantic and Strategy Matching for Persuasive Dialogue
%A Jin, Chuhao
%A Zhu, Yutao
%A Kong, Lingzhen
%A Li, Shijie
%A Zhang, Xiao
%A Song, Ruihua
%A Chen, Xu
%A Chen, Huan
%A Sun, Yuchong
%A Chen, Yu
%A Xu, Jun
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F jin-etal-2023-joint
%X Persuasive dialogue aims to persuade users to achieve some targets by conversations. While previous persuasion models have achieved notable successes, they mostly base themselves on utterance semantic matching, and an important aspect has been ignored, that is, the strategy of the conversations, for example, the agent can choose an emotional-appeal strategy to impress users. Compared with utterance semantics, conversation strategies are high-level concepts, which can be informative and provide complementary information to achieve effective persuasions. In this paper, we propose to build a persuasion model by jointly modeling the conversation semantics and strategies, where we design a BERT-like module and an auto-regressive predictor to match the semantics and strategies, respectively. Experimental results indicate that our proposed approach can significantly improve the state-of-the-art baseline by 5% on a small dataset and 37% on a large dataset in terms of Recall@1. Detailed analyses show that the auto-regressive predictor contributes most to the final performance.
%R 10.18653/v1/2023.findings-emnlp.276
%U https://aclanthology.org/2023.findings-emnlp.276
%U https://doi.org/10.18653/v1/2023.findings-emnlp.276
%P 4187-4197
Markdown (Informal)
[Joint Semantic and Strategy Matching for Persuasive Dialogue](https://aclanthology.org/2023.findings-emnlp.276) (Jin et al., Findings 2023)
ACL
- Chuhao Jin, Yutao Zhu, Lingzhen Kong, Shijie Li, Xiao Zhang, Ruihua Song, Xu Chen, Huan Chen, Yuchong Sun, Yu Chen, and Jun Xu. 2023. Joint Semantic and Strategy Matching for Persuasive Dialogue. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4187–4197, Singapore. Association for Computational Linguistics.