Enhanced Emotion Recognition in Conversations Through Hybrid Context Encoding and Latent Dependency Mining

Zheng Hu, Jiawen Deng, Satoshi Nakagawa, Yan Zhuang, Xiaoyue Zhang, Shimin Cai, Fuji Ren

Abstract: Emotion recognition in conversations (ERC) is a pivotal component of affective computing, involving a common two-stage paradigm where pre-trained language models first extract context-independent features, followed by the encoding of contextual information and the modeling of emotional dependencies. This paradigm faces two challenges: (1) Existing methods struggle to capture both the intra-dialogue emotional continuity and the inter-dialogue semantic similarity. (2) The complexity of emotional elicitation processes gives rise to entangled dependencies, termed “latent dependencies”, which are difficult for current methods to detect and analyze. To overcome these challenges, we propose a Hybrid-Context Encoder with an Automated Latent Dependency Mining model for ERC. Specifically, we examine the emotional continuity and the semantic similarity from the standpoint of context encoders. We experimentally find that context encoders with different architectures exhibit distinct benefits. Based on these findings, we design a hybrid contextual encoding module that effectively combines the strengths of various encoders. Additionally, we design a lightweight generative module for latent dependency mining that autonomously generates a context mask, enabling the effective discovery of latent dependencies. We conduct extensive experiments on three datasets in the text modality.Our model achieves the best performance, which validates the superiority of our approach.

External IDs:dblp:journals/taffco/HuDNZZCR25