%0 Conference Proceedings %T MM-GATBT: Enriching Multimodal Representation Using Graph Attention Network %A Seo, Seung Byum %A Nam, Hyoungwook %A Delgosha, Payam %Y Ippolito, Daphne %Y Li, Liunian Harold %Y Pacheco, Maria Leonor %Y Chen, Danqi %Y Xue, Nianwen %S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop %D 2022 %8 July %I Association for Computational Linguistics %C Hybrid: Seattle, Washington + Online %F seo-etal-2022-mm %X While there have been advances in Natural Language Processing (NLP), their success is mainly gained by applying a self-attention mechanism into single or multi-modalities. While this approach has brought significant improvements in multiple downstream tasks, it fails to capture the interaction between different entities. Therefore, we propose MM-GATBT, a multimodal graph representation learning model that captures not only the relational semantics within one modality but also the interactions between different modalities. Specifically, the proposed method constructs image-based node embedding which contains relational semantics of entities. Our empirical results show that MM-GATBT achieves state-of-the-art results among all published papers on the MM-IMDb dataset. %R 10.18653/v1/2022.naacl-srw.14 %U https://aclanthology.org/2022.naacl-srw.14 %U https://doi.org/10.18653/v1/2022.naacl-srw.14 %P 106-112