Empathetic Response Generation via Emotion Cause Transition Graph

Abstract

Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e.g., emotion status) and cognitive factors (e.g., cause of the emotion). Besides concerning emotion status in early work, the latest approaches study emotion causes in empathetic dialogue. These approaches focus on understanding and duplicating emotion causes in the context to show empathy for the speaker. However, instead of only repeating the contextual causes, the real empathic response often demonstrate a logical and emotion-centered transition from the causes in the context to those in the responses. In this work, we propose an emotion cause transition graph to explicitly model the natural transition of emotion causes between two adjacent turns in empathetic dialogue. With this graph, the concept words of the emotion causes in the next turn can be predicted and used by a specifically designed conceptaware decoder to generate the empathic response. Automatic and human experimental results on the benchmark dataset demonstrate that our method produces more empathetic, coherent, informative, and specific responses than existing models.

UniSA: Unified Generative Framework for Sentiment Analysis

“…Emotion recognition in conversation aims to identify the speaker's emotion from multiple utterances in a conversation [8,31,51,60]. Early research focused on context modeling using GRU [6] models to extract context information and judge the emotion category of the utterance based on the context information [9,12,17,40,49].…”

Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvements

“…Affective empathy oriented methods include mixture of experts (Lin et al, 2019), emotion mimicry (Majumder et al, 2020), and multi-resolution user feedback (Li et al, 2020). Cognitive empathy oriented methods include emotion causes (Gao et al, 2021;Kim et al, 2021;Qian et al, 2023a), empathetic intents (Welivita and Pu, 2020;, external knowledge Sabour et al, 2022;Cai et al, 2023). Besides, models the interaction between knowledge and emotion, Zhao et al (2022) considers self-other awareness, Bi et al (2023) and Kim et al (2022) propose multi-grained and fine-grained levels, respectively.…”