GREEN: Generative Retrieval-Enhanced Emotional Support Conversations

Emotional support dialog systems face computational linguistic challenges as they require a deep understanding of explicit utterances and implicit emotional needs. In particular, existing models have shown limitations in effectively capturing subtle emotional contexts, which are essential for providing meaningful emotional support. To address this, we propose Generative Retrieval-Enhanced Emotional Support Conversations (GREEN), an emotional support dialog model using generative retrieval. Inspired by docID, GREEN introduces a Residual Identifier (ResID), enabling the dynamic identification of emotional context and appropriate support strategies from seeker utterances. By approaching emotional support as a context prediction task, our model works to understand both the explicit meaning of utterances and the underlying emotional needs of seekers. GREEN achieves significant improvements over SOTA models on ESConv with over 25% gains in response diversity metrics, 8.3% in content quality (BLEU-4), and 9.8% in strategy prediction accuracy. Our approach integrates generative retrieval with ResID-based context analysis, advancing emotional support dialog systems. For balanced reporting, we note current limitations—ResID stability under quantization/clustering and ambiguity when misidentification occurs—and plan to improve semantic matching and identifier design with broader real-world validation.