The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer
Open accessAbstract
In this paper, we study Multimodal Named Entity Recognition (MNER) for social media posts.Existing approaches for MNER mainly suffer from two drawbacks: (1) despite generating word-aware visual representations, their word representations are insensitive to the visual context; (2) most of them ignore the bias brought by the visual context.To tackle the first issue, we propose a multimodal interaction module to obtain both image-aware word representations and word-aware visual representations.To alleviate the visual bias, we further propose to leverage purely text-based entity span detection as an auxiliary module, and design a Unified Multimodal Transformer to guide the final predictions with the entity span predictions.Experiments show that our unified approach achieves the new state-of-the-art performance on two benchmark datasets.
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Cites
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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Neural Architectures for Named Entity Recognition
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Incorporating non-local information into information extraction systems by Gibbs sampling
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Metadata
- Authors
- Yu, J., Jiang, J., Yang, L., & Xia, R.
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 3342-3352
- OpenAlex
- W3035448883
- Cited by
- 281
- References
- 38
- FWCI
- 8.11
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Yu, J., Jiang, J., Yang, L., & Xia, R. (2020). Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.306
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