TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in Conversation
Taeyang Yun, Hyunkuk Lim, Jeonghwan Lee, Min Song
Abstract
Emotion Recognition in Conversation (ERC) plays a crucial role in enabling dialogue sys- tems to effectively respond to user requests. The emotions in a conversation can be identi- fied by the representations from various modal- ities, such as audio, visual, and text. How- ever, due to the weak contribution of non-verbal modalities to recognize emotions, multimodal ERC has always been considered a challenging task. In this paper, we propose Teacher-leading Multimodal fusion network for ERC (TelME). TelME incorporates cross-modal knowledge distillation to transfer information from a lan- guage model acting as the teacher to the non- verbal students, thereby optimizing the efficacy of the weak modalities. We then combine multi- modal features using a shifting fusion approach in which student networks support the teacher. TelME achieves state-of-the-art performance in MELD, a multi-speaker conversation dataset for ERC. Finally, we demonstrate the effec- tiveness of our components through additional experiments.
BibTeX
@inproceedings{yun-etal-2024-telme,
title = "{T}el{ME}: Teacher-leading Multimodal Fusion Network for Emotion Recognition in Conversation",
author = "Yun, Taeyang and
Lim, Hyunkuk and
Lee, Jeonghwan and
Song, Min",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.5/",
doi = "10.18653/v1/2024.naacl-long.5",
pages = "82--95"
}