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        <title>Multi Task Learning based Framework for Multimodal Classification</title>
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            <title>Proceedings of the Third Workshop on Multimodal Artificial Intelligence</title>
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            <namePart type="family">Zadeh</namePart>
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    <abstract>Large-scale multi-modal classification aim to distinguish between different multi-modal data, and it has drawn dramatically attentions since last decade. In this paper, we propose a multi-task learning-based framework for the multimodal classification task, which consists of two branches: multi-modal autoencoder branch and attention-based multi-modal modeling branch. Multi-modal autoencoder can receive multi-modal features and obtain the interactive information which called multi-modal encoder feature, and use this feature to reconstitute all the input data. Besides, multi-modal encoder feature can be used to enrich the raw dataset, and improve the performance of downstream tasks (such as classification task). As for attention-based multimodal modeling branch, we first employ attention mechanism to make the model focused on important features, then we use the multi-modal encoder feature to enrich the input information, achieve a better performance. We conduct extensive experiments on different dataset, the results demonstrate the effectiveness of proposed framework.</abstract>
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    <identifier type="doi">10.18653/v1/2021.maiworkshop-1.5</identifier>
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%0 Conference Proceedings
%T Multi Task Learning based Framework for Multimodal Classification
%A Zeng, Danting
%Y Zadeh, Amir
%Y Morency, Louis-Philippe
%Y Liang, Paul Pu
%Y Ross, Candace
%Y Salakhutdinov, Ruslan
%Y Poria, Soujanya
%Y Cambria, Erik
%Y Shi, Kelly
%S Proceedings of the Third Workshop on Multimodal Artificial Intelligence
%D 2021
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F zeng-2021-multi
%X Large-scale multi-modal classification aim to distinguish between different multi-modal data, and it has drawn dramatically attentions since last decade. In this paper, we propose a multi-task learning-based framework for the multimodal classification task, which consists of two branches: multi-modal autoencoder branch and attention-based multi-modal modeling branch. Multi-modal autoencoder can receive multi-modal features and obtain the interactive information which called multi-modal encoder feature, and use this feature to reconstitute all the input data. Besides, multi-modal encoder feature can be used to enrich the raw dataset, and improve the performance of downstream tasks (such as classification task). As for attention-based multimodal modeling branch, we first employ attention mechanism to make the model focused on important features, then we use the multi-modal encoder feature to enrich the input information, achieve a better performance. We conduct extensive experiments on different dataset, the results demonstrate the effectiveness of proposed framework.
%R 10.18653/v1/2021.maiworkshop-1.5
%U https://aclanthology.org/2021.maiworkshop-1.5
%U https://doi.org/10.18653/v1/2021.maiworkshop-1.5
%P 30-35

Markdown (Informal)

[Multi Task Learning based Framework for Multimodal Classification](https://aclanthology.org/2021.maiworkshop-1.5) (Zeng, maiworkshop 2021)

• Multi Task Learning based Framework for Multimodal Classification (Zeng, maiworkshop 2021)

ACL

• Danting Zeng. 2021. Multi Task Learning based Framework for Multimodal Classification. In Proceedings of the Third Workshop on Multimodal Artificial Intelligence, pages 30–35, Mexico City, Mexico. Association for Computational Linguistics.