Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis
Yao-Hung Hubert Tsai, Martin Ma, Muqiao Yang, Ruslan Salakhutdinov, Louis-Philippe Morency
Abstract
The human language can be expressed through multiple sources of information known as modalities, including tones of voice, facial gestures, and spoken language. Recent multimodal learning with strong performances on human-centric tasks such as sentiment analysis and emotion recognition are often black-box, with very limited interpretability. In this paper we propose, which dynamically adjusts weights between input modalities and output representations differently for each input sample. Multimodal routing can identify relative importance of both individual modalities and cross-modality factors. Moreover, the weight assignment by routing allows us to interpret modality-prediction relationships not only globally (i.e. general trends over the whole dataset), but also locally for each single input sample, meanwhile keeping competitive performance compared to state-of-the-art methods.- Anthology ID:
- 2020.emnlp-main.143
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1823–1833
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.143
- DOI:
- 10.18653/v1/2020.emnlp-main.143
- Cite (ACL):
- Yao-Hung Hubert Tsai, Martin Ma, Muqiao Yang, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2020. Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1823–1833, Online. Association for Computational Linguistics.
- Cite (Informal):
- Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis (Tsai et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.emnlp-main.143.pdf
- Code
- martinmamql/multimodal_routing
- Data
- CMU-MOSEI, IEMOCAP