Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shifts

Autor:	Agarwal, Harsh, Bansal, Keshav, Joshi, Abhinav, Modi, Ashutosh
Rok vydání:	2021
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Emotion Recognition in Conversations (ERC) is an important and active research area. Recent work has shown the benefits of using multiple modalities (e.g., text, audio, and video) for the ERC task. In a conversation, participants tend to maintain a particular emotional state unless some stimuli evokes a change. There is a continuous ebb and flow of emotions in a conversation. Inspired by this observation, we propose a multimodal ERC model and augment it with an emotion-shift component that improves performance. The proposed emotion-shift component is modular and can be added to any existing multimodal ERC model (with a few modifications). We experiment with different variants of the model, and results show that the inclusion of emotion shift signal helps the model to outperform existing models for ERC on MOSEI and IEMOCAP datasets. Comment: 13 pages, Accepted at Workshop on Performance and Interpretability Evaluations of Multimodal, Multipurpose, Massive-Scale Models, COLING 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.01938 Zobrazit plný text záznamu View this record from Arxiv