A Personalized Model Integrating Personality for Joint Dialogue Emotion Classification and Act Recognition

Abstract

The joint dialogue emotion classification and act recognition tasks essentially involves the simultaneous classification of emotional and act labels for each utterance in a dialogue. Existing dual-tasks models can recognize emotion and act based on utterance context but overlook the interaction between discourse under the influence of personality and the correlation between dual-tasks. Utter- ances within the same personality and across different personalities exhibit explicit temporal dependencies in the past and future. However, existing models fail to explicitly address the temporal relationships in the interaction between personality and dual-tasks, resulting in a reasoning gap in the temporal corre- lation of interactions under long-term personality. In this paper, A Personalized Model Integrating Personality for Joint Dialogue Emotion Classification and Act RecognitionPEDR-GATis proposed to solve these problems.Firstly, we uti- lize personality information and label distributions to provide initial estimates in semantics, strengthening the association between local semantics guided by personality and task labels. Secondly, the model employs graph attention mech- anisms to extract global semantic temporal information and causal temporal relationships between the two tasks. Finally, PEDR-GAT, by acquiring initial local label information guided by personality and integrating personality-guided global semantic label information, introduces a common attention mechanism to maintain long-term dependencies and supplement short-term information.The experiments were conducted on three benchmark datasets: Mastodon social network (Mastodon), A Manually Labelled Multi-turn Dialogue Dataset (Dai- lyDialog), and A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI (CPED). Compared to the baseline models, there were improvements in F1 scores for emotion classification and act recognition by 1.9% and 0.7% (Mastodon),3.3% and 1.3% (DailyDialog), and 5.3% and 2.3%(CPED), respectively. The results indicate that personality-guided semantic label acquisition enhances the association between semantics and labels, and the graph attention mechanism with personality fusion reasoning can stimulate the cognitive system’s understanding of potential causal information in abstraction.