Improving Multimodal Named Entity Recognition via Entity ...

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3342 3352 July 5 - 10, 2020 Association for Computational Linguistics3342 Improving Multimodal Named Entity Recognition via Entity SpanDetection with Unified Multimodal TransformerJianfei Yu1, Jing Jiang2, Li Yang3, and Rui Xia1, 1 School of Artificial Intelligence, Nanjing University of Science & Technology, China2 School of Information Systems, Singapore Management University, Singapore3 DBS Bank, Singapore{jfyu, this paper, we study Multimodal NamedEntity Recognition (MNER) for social mediaposts. Existing approaches for MNER mainlysuffer from two drawbacks: (1) despite gener-ating word-aware visual representations, theirword representations are insensitive to the vi-sual context; (2) most of them ignore the biasbrought by the visual context.}

with cross-modal attention mechanism to produce an image-aware word representation and a word-aware visual representation for each input word, respectively. Finally, to largely eliminate the bias of the visual context, we propose to leverage text-based entity span detection as an auxiliary task, and design a unified neural architecture based on

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