@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://preview.aclanthology.org/ingest-emnlp/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 ``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"
}