CAPE: A Chinese Dataset for Appraisal-based Emotional Generation using Large Language Models

TL;DR

This paper introduces CAPE, a Chinese dataset based on Cognitive Appraisal theory, designed to improve emotional response generation in conversational AI by capturing complex human emotions and context.

Contribution

The study presents a novel two-stage data generation framework for creating an emotion-rich Chinese dataset, enabling more emotionally aware dialogue systems.

Findings

Agents trained on CAPE produce more human-like emotional responses

Automated and human evaluations confirm improved emotional alignment

CAPE facilitates emotion prediction and next utterance prediction tasks

Abstract

Generating emotionally appropriate responses in conversations with large language models presents a significant challenge due to the complexities of human emotions and cognitive processes, which remain largely underexplored in their critical role in social interactions. In this study, we introduce a two-stage automatic data generation framework to create CAPE, a Chinese dataset named Cognitive Appraisal theory-based Emotional corpus. This corpus facilitates the generation of dialogues with contextually appropriate emotional responses by accounting for diverse personal and situational factors. We propose two tasks utilizing this dataset: emotion prediction and next utterance prediction. Both automated and human evaluations demonstrate that agents trained on our dataset can deliver responses that are more aligned with human emotional expressions. Our study shows the potential for advancing emotional expression in conversational agents, paving the way for more nuanced and meaningful human-computer interactions.