Towards Multimodal Empathetic Response Generation: A Rich Text-Speech-Vision Avatar-based Benchmark

TL;DR

This paper introduces AvaMERG, a large-scale multimodal benchmark dataset for empathetic response generation using text, speech, and avatar videos, and presents Empatheia, an end-to-end model that outperforms baselines in this task.

Contribution

It pioneers the multimodal ERG task by creating a new benchmark dataset and developing a novel end-to-end model with multimodal reasoning and empathetic tuning strategies.

Findings

Empatheia outperforms baseline methods on AvaMERG.

Multimodal inputs improve empathetic response quality.

The benchmark enables future research in multimodal ERG.

Abstract

Empathetic Response Generation (ERG) is one of the key tasks of the affective computing area, which aims to produce emotionally nuanced and compassionate responses to user's queries. However, existing ERG research is predominantly confined to the singleton text modality, limiting its effectiveness since human emotions are inherently conveyed through multiple modalities. To combat this, we introduce an avatar-based Multimodal ERG (MERG) task, entailing rich text, speech, and facial vision information. We first present a large-scale high-quality benchmark dataset, \textbf{AvaMERG}, which extends traditional text ERG by incorporating authentic human speech audio and dynamic talking-face avatar videos, encompassing a diverse range of avatar profiles and broadly covering various topics of real-world scenarios. Further, we deliberately tailor a system, named \textbf{Empatheia}, for MERG. Built upon a Multimodal Large Language Model (MLLM) with multimodal encoder, speech and avatar generators, Empatheia performs end-to-end MERG, with Chain-of-Empathetic reasoning mechanism integrated for enhanced empathy understanding and reasoning. Finally, we devise a list of empathetic-enhanced tuning strategies, strengthening the capabilities of emotional accuracy and content, avatar-profile consistency across modalities. Experimental results on AvaMERG data demonstrate that Empatheia consistently shows superior performance than baseline methods on both textual ERG and MERG. Overall, this work is expected to pioneer the MERG research by introducing a novel benchmark and an end-to-end model, laying a solid foundation for future advancements in multimodal empathetic response generation.