@inproceedings{tsai-etal-2020-multimodal,
title = "Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis",
author = "Tsai, Yao-Hung Hubert and
Ma, Martin and
Yang, Muqiao and
Salakhutdinov, Ruslan and
Morency, Louis-Philippe",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.143",
doi = "10.18653/v1/2020.emnlp-main.143",
pages = "1823--1833",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis
%A Tsai, Yao-Hung Hubert
%A Ma, Martin
%A Yang, Muqiao
%A Salakhutdinov, Ruslan
%A Morency, Louis-Philippe
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F tsai-etal-2020-multimodal
%X 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.
%R 10.18653/v1/2020.emnlp-main.143
%U https://aclanthology.org/2020.emnlp-main.143
%U https://doi.org/10.18653/v1/2020.emnlp-main.143
%P 1823-1833
Markdown (Informal)
[Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis](https://aclanthology.org/2020.emnlp-main.143) (Tsai et al., EMNLP 2020)
ACL