Fusing pairwise modalities for emotion recognition in conversations

Abstract

Multimodal fusion has the potential to significantly enhance model performance in the domain of Emotion Recognition in Conversations (ERC) by efficiently integrating information from diverse modalities. However, existing methods face challenges as they directly integrate information from different modalities, making it difficult to assess the individual impact of each modality during training and to capture nuanced fusion. To deal with it, we propose a novel framework named Fusing Pairwise Modalities for ERC. In this proposed method, the pairwise fusion technique is incorporated into multimodal fusion to enhance model performance, which enables each modality to contribute unique information, thereby facilitating a more comprehensive understanding of the emotional context. Additionally, a designed density loss is applied to characterise fused feature density, with a specific focus on mitigating redundancy in pairwise fusion methods. The density loss penalises feature density during training, contributing to a more efficient and effective fusion process. To validate the proposed framework, we conduct comprehensive experiments on two benchmark datasets, namely IEMOCAP and MELD. The results demonstrate the superior performance of our approach compared to state-of-the-art methods, indicating its effectiveness in addressing challenges related to multimodal fusion in the context of ERC.