EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamics
<|reference_start|>EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamics: Designing emotionally intelligent conversational systems to provide comfort and advice to people experiencing distress is a compelling area of research. Previous efforts have focused on developing modular dialogue systems that treat socio-emotional strategy prediction as an auxiliary task and generate strategy-conditioned responses with customized decoders. Recently, with advancements in large language models (LLMs), end-to-end dialogue agents without explicit socio-emotional strategy prediction steps have become prevalent. However, despite their excellence in language generation, recent studies show that LLMs' inherent preference bias towards certain socio-emotional strategies hinders the delivery of high-quality emotional support. To address this challenge, we propose decoupling strategy prediction from language generation, and introduce a novel dialogue strategy predictor, EmoDynamiX, which models the discourse dynamics between user emotions and system strategies using a heterogeneous graph. Additionally, we make use of the Emotion Recognition in Conversations (ERC) task and design a flexible mixed-emotion module to capture fine-grained emotional states of the user. Experimental results on two ESC datasets show EmoDynamiX outperforms previous state-of-the-art methods with a significant margin.<|reference_end|>
@article{wan2024emodynamix:,
    title={EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling
  MiXed Emotions and Discourse Dynamics},
    author={Chenwei Wan, Matthieu Labeau, Chlo'e Clavel},
    journal={arXiv preprint arXiv:2408.08782},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08782},
    primaryClass={cs.CL}
}