Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation

• URL: http://arxiv.org/abs/2507.15205v2
• Date: Thu, 24 Jul 2025 05:15:18 GMT
• Title: Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation
• Authors: Xinran Li, Xiujuan Xu, Jiaqi Qiao,
• Abstract summary: This paper proposes a novel multimodal approach to Emotion Recognition in Conversation (ERC)<n>It constructs a long-distance graph neural network and a short-distance graph neural network to obtain multimodal features of distant and nearby utterances.<n> Experimental results on the IEMOCAP and MELD datasets demonstrate that our model outperforms existing benchmarks.
• Score: 3.031375888004876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emotion Recognition in Conversation (ERC) is a practical and challenging task. This paper proposes a novel multimodal approach, the Long-Short Distance Graph Neural Network (LSDGNN). Based on the Directed Acyclic Graph (DAG), it constructs a long-distance graph neural network and a short-distance graph neural network to obtain multimodal features of distant and nearby utterances, respectively. To ensure that long- and short-distance features are as distinct as possible in representation while enabling mutual influence between the two modules, we employ a Differential Regularizer and incorporate a BiAffine Module to facilitate feature interaction. In addition, we propose an Improved Curriculum Learning (ICL) to address the challenge of data imbalance. By computing the similarity between different emotions to emphasize the shifts in similar emotions, we design a "weighted emotional shift" metric and develop a difficulty measurer, enabling a training process that prioritizes learning easy samples before harder ones. Experimental results on the IEMOCAP and MELD datasets demonstrate that our model outperforms existing benchmarks.

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