Source-linked AI summary
MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation
Quan Tu, Yanran Li, Jianwei Cui, Bin Wang, Ji-Rong Wen, Rui Yan
TL;DR
Existing emotional support systems use coarse conversation-level emotion labels and often emphasize empathy without modeling gradual distress reduction. MISC addresses these gaps with COMET-enhanced fine-grained mental-state understanding and mixed strategy generation, and experiments on ESConv report more relevant and empathetic responses than five state-of-the-art empathetic chatbots. The paper also notes that sensitive or emotionally triggering language may remain in the dataset and makes no treatment or diagnosis claims for risky situations such as self-harm.
Problem
Existing methods use coarse conversation-level emotion labels and focus on expressing empathy rather than gradually reducing seekers’ distress in emotional support conversations.
Method
MISC uses attention over COMET knowledge tuples for fine-grained mental-state understanding and represents response strategy as a probability distribution over a strategy codebook.
Results
MISC produces more relevant and empathetic responses than five state-of-the-art empathetic chatbots on the ESConv benchmark under automatic metrics and manual judgments.
Takeaways & Limitations
Fine-grained mental-state modeling and mixed response strategy incorporation are reported as important to MISC’s effectiveness in emotional support conversation.
Takeaways & Limitations
The dataset may still contain emotionally triggering language, and the paper makes no treatment or diagnosis claims for risky self-harm-related conversations.
Abstract
from arXiv · showhide
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}.
1 Introduction
MISC targets emotional support conversation by modeling the seeker’s fine-grained, changing mental state and generating responses that combine multiple strategies. Experiments on ESConv show that MISC produces more relevant and empathetic responses than five state-of-the-art empathetic chatbots.
- Motivation: Emotional support conversation requires gradually reducing a seeker’s distress, unlike approaches that primarily express empathy in responses.The paper identifies this goal as a key distinction between emotional support and general empathetic chatting.
- Motivation: Conversation-level emotion labels are coarse-grained and static, so MISC models the seeker’s fine-grained mental state at each utterance.User emotion intensity can change as the conversation develops.
- Approach: MISC selectively adopts COMET knowledge tuples through attention to capture the seeker’s instantaneous mental state.COMET is used as a pretrained generative commonsense reasoning model.
- Approach: MISC represents response strategy as a probability distribution over a strategy codebook and generates responses using a mixture of strategies.This replaces one-hot strategy modeling and supports gradual transitions within longer responses.
- Results: Responses generated by MISC are more relevant and empathetic than those of five state-of-the-art empathetic chatbots on ESConv.The comparison uses both automatic metrics and manual judgments.
- Contributions: The paper’s contributions combine commonsense knowledge and mixed response strategy in a Seq2Seq model, evaluated against other state-of-the-art methods.Additional analyses examine strategy modeling and its influence on chatbot empathy.
2 Related Work
The related work distinguishes emotional support from other emotion-aware dialogue settings and motivates combining commonsense reasoning with strategy-aware response generation. MISC uses COMET to capture seeker mental states and support strategy prediction.
- Emotion-aware dialogue: Emotion-aware dialogue research includes emotional chatting, empathetic responding, and emotional support conversation as distinct categories.Earlier work focused on emotional signals, while later work addressed specific emotions and deeper emotion understanding.
- Commonsense knowledge: MISC differs from prior approaches by using COMET to capture seeker mental states and facilitate strategy prediction in emotional support conversation.COMET is a generative commonsense model used specifically for this setting.
- Commonsense knowledge: ATOMIC provides social knowledge about event-centered causes and effects and person-related mental states, unlike ConceptNet’s physical-knowledge emphasis.The paper uses COMET, trained over ATOMIC, for emotional support conversation.
- Conversation strategy: Conversation strategy research uses notions including dialog acts, response intention, and communication strategy across task-oriented, social, and empathetic dialogue.Dialog act schemes have been empirically validated in several dialogue settings.
3 Preliminaries
MISC is evaluated on ESConv, where seekers provide emotion types and situations and supporters’ utterances carry strategy labels. In this setting, response generation conditions on dialogue context, seeker situation, and the seeker’s last utterance.
- ESConv Dataset: ESConv records seeker emotion types, free-form situations, and the strategy associated with every supporter utterance.The dataset contains eight nearly evenly distributed strategies.
- Problem Formulation: General dialogue response generation estimates p(r|c), the probability distribution of a response given dialogue context.The dataset is represented as context-response pairs.
- Problem Formulation: In emotional support conversation, the seeker’s situation s is added as an input alongside dialogue context c and last utterance x.The target distribution becomes p(r|c, s, x).
4 Model: MISC
MISC combines commonsense-enhanced mental-state encoding, mixed response-strategy learning, and multi-factor-aware decoding for emotional support conversation.
- MISC has three components: a mental state-enhanced encoder, a mixed strategy learning module, and a multi-factor-aware decoder.
- Mental State-Enhanced Encoder: COMET generates commonsense mental-state information from the situation and the seeker’s last post using predefined relations and multiple free-form tails.The resulting mental-state blocks are encoded into dense vectors.
- Mental State-Enhanced Encoder: An attention mechanism refines COMET-derived blocks by emphasizing those strongly relevant to the context.
- Mixed Strategy Learning Module: The model represents response strategy as a probability distribution over a codebook rather than a one-hot indicator, producing a weighted strategy representation.The codebook contains m = 8 strategy latent vectors, and weighting them by strategy probabilities yields the representation h_g.
- Mixed Strategy Learning Module: The mixed-strategy representation is intended to support long responses that gradually reduce distress, while allowing higher-probability strategies to exert greater influence.With a sharp strategy distribution, a single strategy can dominate control.
- Multi-Factor-Aware Decoder: The decoder generates responses by interacting with multiple factors, while joint training minimizes a combined strategy-prediction and response-generation objective.The objective combines the response loss L_r and strategy loss L_g.
5 Experiments
Experiments on ESConv compare MISC with empathetic-chatbot baselines using automatic and human evaluations. MISC performs best on almost every metric, with stronger strategy prediction, relevance, empathy, and contextual knowledge.
- Experimental setup: The experiments evaluate MISC and five state-of-the-art empathetic chatbots on the ESConv benchmark using automatic and human judgments.Automatic evaluation includes strategy accuracy, perplexity, BLEU, ROUGE-L, METEOR, and diversity; human evaluation covers fluency, knowledge, and related qualities.
- Automatic results: MISC models mixed response strategies, whereas BlenderBot-Joint predicts one strategy at the first decoding step.The comparison tests whether distributed strategy representations better support emotional-support response generation.
- Automatic results: MISC is more effective than BlenderBot-Joint, especially in predicting response strategies accurately.The result supports treating response strategy as an additional modeling target in emotional support conversation.
- Human evaluation: Human evaluation is consistent with the automatic results, with MISC producing highly fluent and contextually knowledgeable responses.MISC obtains the highest Knowledge score, while MISC and BlenderBot-Joint significantly outperform other models on Fluency.
- Overall findings: MISC performs best on almost every metric, highlighting the importance of fine-grained mental-state modeling and mixed response strategies.The conclusion is based on the reported automatic and human evaluation results.
6 Analysis
The analysis examines MISC through ablations, examples, and strategy-distribution comparisons. Results support fine-grained mental-state information and mixed strategies for context-sensitive, smoothly transitioning emotional support.
- 6.1 Ablation Study: Removing the strategy-attention component causes dramatic decreases across all reported metrics, indicating its importance to MISC.The ablation study removes components g, s, and x and observes the largest reported degradation when g is ablated.
- 6.3 Fine-grained Emotion Understanding: Replacing COMET-based fine-grained mental information with a coarse-grained emotion label lowers all reported metrics.The comparison uses MISE, which adds an emotion-classification objective instead of COMET blocks.
- 6.3 Fine-grained Emotion Understanding: Attended COMET blocks provide mental-state knowledge that helps MISC express the seeker’s feelings and support strategy-aware responses.The xReact/hurt and xAttr/sad blocks support the response phrase “it was painful”; blocks retrieved from situation and last-post information contain distinct mental-state knowledge.
- 6.4 Mixed-Strategy-Aware Empathetic Responding: Mixed strategies help MISC organize smoother emotional support, such as combining self-disclosure with reflection of feelings after recognizing sadness.The example describes a response that first reveals similar experience and then adds detailed information.
- 6.4 Mixed-Strategy-Aware Empathetic Responding: MISC’s top-k strategy prediction accuracy always surpasses BlenderBot-Joint’s, with top-5 accuracy exceeding 80%.BlenderBot-Joint is treated as a single-strategy model, while MISC uses a mixed strategy representation.
- 6.4 Mixed-Strategy-Aware Empathetic Responding: MISC’s strategy distribution more closely matches the ESConv ground-truth distribution and is less likely than BlenderBot-Joint to predict Others.This pattern appears across almost all conversation stages and is interpreted as more discriminative strategy learning suitable for the ESC framework.
7 Conclusions
MISC combines COMET-based instant mental-state understanding with mixed strategy-aware decoding for emotional support conversation. Experiments support the model’s effectiveness, while future work targets dynamically learned mixed strategies.
- MISC introduces COMET to capture the seeker’s instant mental state and uses a mixed strategy-aware decoder to generate supportive responses.
8 Ethical Considerations
The ESConv dataset is publicly available and filters personally identifiable information, but incomplete filtering may leave emotionally triggering language. The system is not claimed to provide treatment or diagnosis for risky self-harm-related situations.
- The dataset providers filtered sensitive information, including personally identifiable information, before release.
- Incomplete filtering coverage may leave emotionally triggering language in the conversations.
- MISC is intended for emotional support conversational agents, not treatment or diagnosis in self-harm-related conversations.
A Distribution of Strategies
The original ESConv dataset has a relatively balanced distribution across response strategies.
- The proportions of the strategies in the original ESConv dataset are relatively balanced.
B Definition of Strategies
The strategy definitions describe ways supporters elicit, rephrase, inform, and otherwise support help-seekers. Question Asking seeks problem-related information, while Restatement or Paraphrasing rephrases the help-seeker’s statements.
- Question Asking requests information about the problem so the help-seeker can articulate the issues they face.Open-ended questions are preferred, while closed questions can obtain specific information.
- Restatement or Paraphrasing concisely rephrases the help-seeker’s statements.
- Information provides useful data, facts, opinions, resources, or answers to the help-seeker’s questions.
- Others covers pleasantries and support strategies outside the other categories.
C Description of COMET Relations
The section describes COMET relations that represent event effects, reactions, desires, attributes, intentions, and needs involving Person X and others.
- COMET includes oEffect, oReact, and oWant relations for effects, reactions, and desired actions of others besides Person X.
- COMET includes xAttr and xEffect relations for describing Person X and representing how an event affects Person X.
- COMET includes xIntent and xNeed relations concerning why Person X causes an event and what Person X needs.