MERIA: Empathetic Response Generation via Parallel Disentanglement and Uncertainty-Gated Fusion

摘要

A critical challenge in advancing human-like conversational AI systems is enabling models to understand and respond to user emotions contextually, a task known as Multimodal Empathetic Response Generation (MERG). While prevailing multimodal models attempt to resolve cross-modal emotional discrepancies via concatenation or cross-attention, their simplistic fusion mechanisms often fail to account for the nuanced and contradictory nature of human emotions. Consequently, the resulting feature representations suffer from these unresolved internal conflicts, limiting their effectiveness. In this paper, we propose a novel Multimodal Empathetic Reasoning and Inconsistency-Aware (MERIA) framework. MERIA introduces a multimodal disentanglement encoder based on β-VAE and extends the AvaMERG dataset with multimodal chains of empathy (M-CoE). Our framework outperforms existing methods, achieving the best human evaluation scores in the empathetic text response generation task of the MERG 2025 Challenge. Our code is available at https://github.com/DANG-ai/MERIA-MERG.