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    <abstract>The Transformer has proven to be a powerful feature extraction method and has gained widespread adoption in natural language processing (NLP). In this paper we propose a multimodal item categorization (MIC) system solely based on the Transformer for both text and image processing. On a multimodal product data set collected from a Japanese e-commerce giant, we tested a new image classification model based on the Transformer and investigated different ways of fusing bi-modal information. Our experimental results on real industry data showed that the Transformer-based image classifier has performance on par with ResNet-based classifiers and is four times faster to train. Furthermore, a cross-modal attention layer was found to be critical for the MIC system to achieve performance gains over text-only and image-only models.</abstract>
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%0 Conference Proceedings
%T Multimodal Item Categorization Fully Based on Transformer
%A Chen, Lei
%A Chou, Houwei
%A Xia, Yandi
%A Miyake, Hirokazu
%Y Malmasi, Shervin
%Y Kallumadi, Surya
%Y Ueffing, Nicola
%Y Rokhlenko, Oleg
%Y Agichtein, Eugene
%Y Guy, Ido
%S Proceedings of the 4th Workshop on e-Commerce and NLP
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F chen-etal-2021-multimodal-item
%X The Transformer has proven to be a powerful feature extraction method and has gained widespread adoption in natural language processing (NLP). In this paper we propose a multimodal item categorization (MIC) system solely based on the Transformer for both text and image processing. On a multimodal product data set collected from a Japanese e-commerce giant, we tested a new image classification model based on the Transformer and investigated different ways of fusing bi-modal information. Our experimental results on real industry data showed that the Transformer-based image classifier has performance on par with ResNet-based classifiers and is four times faster to train. Furthermore, a cross-modal attention layer was found to be critical for the MIC system to achieve performance gains over text-only and image-only models.
%R 10.18653/v1/2021.ecnlp-1.13
%U https://aclanthology.org/2021.ecnlp-1.13
%U https://doi.org/10.18653/v1/2021.ecnlp-1.13
%P 111-115

Markdown (Informal)

[Multimodal Item Categorization Fully Based on Transformer](https://aclanthology.org/2021.ecnlp-1.13) (Chen et al., ECNLP 2021)

• Multimodal Item Categorization Fully Based on Transformer (Chen et al., ECNLP 2021)

ACL

• Lei Chen, Houwei Chou, Yandi Xia, and Hirokazu Miyake. 2021. Multimodal Item Categorization Fully Based on Transformer. In Proceedings of the 4th Workshop on e-Commerce and NLP, pages 111–115, Online. Association for Computational Linguistics.