Emotion-Aware Dialogue Generation
Overview
This field studies how dialogue systems can recognize emotional cues and generate responses that are empathetic, appropriate, and consistent across turns. Approaches include emotion and strategy conditioning, psychological reasoning, retrieval and memory, multimodal use of prosody or facial cues, and training with human preferences or reinforcement learning. Systems are evaluated on emotional-support and dialogue benchmarks, speech and emotion datasets, and human judgments of empathy, relevance, and naturalness. A central challenge is that emotional understanding and expression remain difficult to measure: responses can seem formulaic, misread a user, or lose emotional and persona consistency over longer interactions.
Latest papers
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