@inproceedings{li-etal-2024-helpful,
title = "Be Helpful but Don`t Talk too Much - Enhancing Helpfulness in Conversations through Relevance in Multi-Turn Emotional Support",
author = "Li, Junlin and
Peng, Bo and
Hsu, Yu-Yin and
Huang, Chu-Ren",
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.118/",
doi = "10.18653/v1/2024.emnlp-main.118",
pages = "1976--1988",
abstract = "For a conversation to help and support, speakers should maintain an {\textquotedblleft}effect-effort{\textquotedblright} tradeoff. As outlined in the gist of {\textquotedblleft}Cognitive Relevance Principle{\textquotedblright}, helpful speakers should optimize the {\textquotedblleft}cognitive relevance{\textquotedblright} through maximizing the {\textquotedblleft}cognitive effects{\textquotedblright} and minimizing the {\textquotedblleft}processing effort{\textquotedblright} imposed on listeners. Although preference learning methods have given rise a boon of studies in pursuit of{\textquotedblleft}effect-optimization{\textquotedblright}, none have delved into the critical {\textquotedblleft}effort-optimiazation{\textquotedblright} to fully cultivate the awareness of {\textquotedblleft}optimal relevance{\textquotedblright} into thecognition of conversation agents. To address this gap, we integrate the {\textquotedblleft}Cognitive Relevance Principle{\textquotedblright} into emotional support agents in the environment of multi-turn conversation. The results demonstrate a significant and robust improvement against the baseline systems with respect to response quality, human-likedness and supportivenss. This study offers compelling evidence for the effectiveness of the {\textquotedblleft}Relevance Principle{\textquotedblright} in generating human-like, helpful, and harmless emotional support conversations. The source code will be available at https://github.com/CN-Eyetk/VLESA-ORL.git"
}
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<abstract>For a conversation to help and support, speakers should maintain an “effect-effort” tradeoff. As outlined in the gist of “Cognitive Relevance Principle”, helpful speakers should optimize the “cognitive relevance” through maximizing the “cognitive effects” and minimizing the “processing effort” imposed on listeners. Although preference learning methods have given rise a boon of studies in pursuit of“effect-optimization”, none have delved into the critical “effort-optimiazation” to fully cultivate the awareness of “optimal relevance” into thecognition of conversation agents. To address this gap, we integrate the “Cognitive Relevance Principle” into emotional support agents in the environment of multi-turn conversation. The results demonstrate a significant and robust improvement against the baseline systems with respect to response quality, human-likedness and supportivenss. This study offers compelling evidence for the effectiveness of the “Relevance Principle” in generating human-like, helpful, and harmless emotional support conversations. The source code will be available at https://github.com/CN-Eyetk/VLESA-ORL.git</abstract>
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%0 Conference Proceedings
%T Be Helpful but Don‘t Talk too Much - Enhancing Helpfulness in Conversations through Relevance in Multi-Turn Emotional Support
%A Li, Junlin
%A Peng, Bo
%A Hsu, Yu-Yin
%A Huang, Chu-Ren
%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 li-etal-2024-helpful
%X For a conversation to help and support, speakers should maintain an “effect-effort” tradeoff. As outlined in the gist of “Cognitive Relevance Principle”, helpful speakers should optimize the “cognitive relevance” through maximizing the “cognitive effects” and minimizing the “processing effort” imposed on listeners. Although preference learning methods have given rise a boon of studies in pursuit of“effect-optimization”, none have delved into the critical “effort-optimiazation” to fully cultivate the awareness of “optimal relevance” into thecognition of conversation agents. To address this gap, we integrate the “Cognitive Relevance Principle” into emotional support agents in the environment of multi-turn conversation. The results demonstrate a significant and robust improvement against the baseline systems with respect to response quality, human-likedness and supportivenss. This study offers compelling evidence for the effectiveness of the “Relevance Principle” in generating human-like, helpful, and harmless emotional support conversations. The source code will be available at https://github.com/CN-Eyetk/VLESA-ORL.git
%R 10.18653/v1/2024.emnlp-main.118
%U https://aclanthology.org/2024.emnlp-main.118/
%U https://doi.org/10.18653/v1/2024.emnlp-main.118
%P 1976-1988
Markdown (Informal)
[Be Helpful but Don’t Talk too Much - Enhancing Helpfulness in Conversations through Relevance in Multi-Turn Emotional Support](https://aclanthology.org/2024.emnlp-main.118/) (Li et al., EMNLP 2024)
ACL