Situation-aware empathetic response generation

Zhou Yang

Abstract:

Empathetic response generation endeavours to perceive the interlocutor’s emotional and cognitive states in the dialogue and express proper responses. Previous studies detect the interlocutor’s states by understanding the immediate context of the dialogue. However, these
methods are at an elementary/intermediate level of empathetic understanding due to the
neglect of the broader context (i.e., the situation) and its associations with the dialogue,
leading to inaccurate comprehension of the interlocutor’s states. In this paper, we utilize the
EMPATHETIC-DIALOGUES dataset consisting of 25k dialogues, and on this basis, we propose a
Situation-Dialogue Association Model (SDAM). SDAM focuses on the broader context, i.e., the
situation, and enhances the understanding of empathy from explicit and implicit associations.
Regarding explicit associations, we propose a bidirectional filtering encoder. It selects relevant
keywords between the situation and dialogue, learning their direct lexical relevance. For implicit
associations, we use a knowledge-based hypergraph network grounded to learn convoluted
connections between the situation and the dialogue. Moreover, we also introduce a simple finetuning approach that combines SDAM with large language models to further strengthen the
empathetic understanding capability. Compared to the baseline, SDAM demonstrates superior
empathetic ability. In terms of emotion accuracy, fluency, and response diversity (Distinct1/Distinct-2), SDAM achieves improvements of 12.25 (a 30.47% increase), 0.3 (a 0.85%
increase), and 0.86/1.23 (116.22% and 30.67% increases), respectively. Additionally, our
variant model based on large language models exhibits better emotion recognition capability
without compromising response quality, specifically achieving an improvement of 0.23 (a 0.37%
increase) in emotion accuracy.