Hierarchical Fusion for Online Multimodal Dialog Act Classification

Md Messal Monem Miah, Adarsh Pyarelal, Ruihong Huang

Abstract
We propose a framework for online multimodal dialog act (DA) classification based on raw audio and ASR-generated transcriptions of current and past utterances. Existing multimodal DA classification approaches are limited by ineffective audio modeling and late-stage fusion. We showcase significant improvements in multimodal DA classification by integrating modalities at a more granular level and incorporating recent advancements in large language and audio models for audio feature extraction. We further investigate the effectiveness of self-attention and cross-attention mechanisms in modeling utterances and dialogs for DA classification. We achieve a substantial increase of 3 percentage points in the F1 score relative to current state-of-the-art models on two prominent DA classification datasets, MRDA and EMOTyDA.
Anthology ID:
2023.findings-emnlp.505
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7532–7545
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.505
DOI:
10.18653/v1/2023.findings-emnlp.505
Bibkey:
Cite (ACL):
Md Messal Monem Miah, Adarsh Pyarelal, and Ruihong Huang. 2023. Hierarchical Fusion for Online Multimodal Dialog Act Classification. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7532–7545, Singapore. Association for Computational Linguistics.
Cite (Informal):
Hierarchical Fusion for Online Multimodal Dialog Act Classification (Miah et al., Findings 2023)
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