ToMESC: A Theory of Mind-based Dataset for Emotional Support Conversations

Task-oriented dialogue systems aim to help users achieve specific goals through natural language interaction. Emotional Support Conversations (ESC), a representative task in this domain, focus on reducing users’ emotional distress while facilitating problem-solving. Recent approaches add commonsense knowledge to prompts for large language models (LLMs) to generate supportive responses. However, they often over-rely on the final utterance, neglect dialogue dynamics, fail to track emotional cues, or treat knowledge types independently—resulting in incoherent or emotionally misaligned outputs. To address these issues, we introduce ToMESC, a Theory of Mind-based dataset that models the latent mental states—Belief, Emotion, Desire, and Intent—of an emotionally supportive assistant in a structured causal sequence. Built upon the ESConv dataset, ToMESC provides turn-level annotations that map mental states to the evolving dialogue context. This structure enables the generation of more coherent and strategically aligned responses. We use ToMESC to train a model that infers the assistant's mental states from dialogue. These predicted states are then used as intermediate reasoning steps to guide response generation in a zero-shot LLM framework. Experiments demonstrate that fine-tuning on ToMESC enhances mental state inference, yielding responses that are more emotionally appropriate and goal-directed than those generated by existing commonsense reasoning baselines.