MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation

• URL: http://arxiv.org/abs/2203.13560v1
• Date: Fri, 25 Mar 2022 10:32:04 GMT
• Title: MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation
• Authors: Quan Tu, Yanran Li, Jianwei Cui, Bin Wang, Ji-Rong Wen and Rui Yan
• Abstract summary: We propose a novel model for emotional support conversation. It infers the user's fine-grained emotional status, and then responds skillfully using a mixture of strategy. Experimental results on the benchmark dataset demonstrate the effectiveness of our method.
• Score: 64.37111498077866
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Applying existing methods to emotional support conversation -- which provides valuable assistance to people who are in need -- has two major limitations: (a) they generally employ a conversation-level emotion label, which is too coarse-grained to capture user's instant mental state; (b) most of them focus on expressing empathy in the response(s) rather than gradually reducing user's distress. To address the problems, we propose a novel model \textbf{MISC}, which firstly infers the user's fine-grained emotional status, and then responds skillfully using a mixture of strategy. Experimental results on the benchmark dataset demonstrate the effectiveness of our method and reveal the benefits of fine-grained emotion understanding as well as mixed-up strategy modeling. Our code and data could be found in \url{https://github.com/morecry/MISC}.

Related papers

• Towards Empathetic Conversational Recommender Systems [77.53167131692]
  We propose an empathetic conversational recommender (ECR) framework. ECR contains two main modules: emotion-aware item recommendation and emotion-aligned response generation. Our experiments on the ReDial dataset validate the efficacy of our framework in enhancing recommendation accuracy and improving user satisfaction.
  arXiv  Detail & Related papers  (2024-08-30T15:43:07Z)
• Towards a Generative Approach for Emotion Detection and Reasoning [0.7366405857677227]
  We introduce a novel approach to zero-shot emotion detection and emotional reasoning using large language models. Our paper is the first work on using a generative approach to jointly address the tasks of emotion detection and emotional reasoning for texts.
  arXiv  Detail & Related papers  (2024-08-09T07:20:15Z)
• APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation [71.26755736617478]
  Empathetic response generation is designed to comprehend the emotions of others. We develop a framework that combines retrieval augmentation and emotional support strategy integration. Our framework can enhance the empathy ability of LLMs from both cognitive and affective empathy perspectives.
  arXiv  Detail & Related papers  (2024-07-23T02:23:37Z)
• ESCoT: Towards Interpretable Emotional Support Dialogue Systems [57.19341456308303]
  Previous works mostly focus on generating better responses but ignore interpretability, which is extremely important for constructing reliable dialogue systems. We propose an emotional support response generation scheme, named $textbfE$motion-Focused and $textbfS$trategy-Driven. We also conduct extensive experiments and human evaluations to validate the effectiveness of the proposed ESCoT and generated dialogue responses.
  arXiv  Detail & Related papers  (2024-06-16T14:37:17Z)
• EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation [34.24557248359872]
  We propose an emotional inertia and contagion-driven dependency modeling approach (EmotionIC) for ERC task. Our EmotionIC consists of three main components, i.e., Identity Masked Multi-Head Attention (IMMHA), Dialogue-based Gated Recurrent Unit (DiaGRU) and Skip-chain Conditional Random Field (SkipCRF) Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark datasets.
  arXiv  Detail & Related papers  (2023-03-20T13:58:35Z)
• Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning [81.79431311952656]
  We propose a novel system MultiESC to provide Emotional Support. For strategy planning, we propose lookaheads to estimate the future user feedback after using particular strategies. For user state modeling, MultiESC focuses on capturing users' subtle emotional expressions and understanding their emotion causes.
  arXiv  Detail & Related papers  (2022-10-09T12:23:47Z)
• CEM: Commonsense-aware Empathetic Response Generation [31.956147246779423]
  We propose a novel approach for empathetic response generation, which leverages commonsense to draw more information about the user's situation. We evaluate our approach on EmpatheticDialogues, which is a widely-used benchmark dataset for empathetic response generation.
  arXiv  Detail & Related papers  (2021-09-13T06:55:14Z)
• Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction [55.47134146639492]
  This article proposes a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post. Experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness.
  arXiv  Detail & Related papers  (2021-06-06T06:26:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.