An Effective Span-based Multimodal Named Entity Recognition with Consistent Cross-Modal Alignment

Yongxiu Xu, Hao Xu, Heyan Huang, Shiyao Cui, Minghao Tang, Longzheng Wang, Hongbo Xu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

With the increasing availability of multimodal content on social media, consisting primarily of text and images, multimodal named entity recognition (MNER) has gained a wide-spread attention. A fundamental challenge of MNER lies in effectively aligning different modalities. However, the majority of current approaches rely on word-based sequence labeling framework and align the image and text at inconsistent semantic levels (whole image-words or regions-words). This misalignment may lead to inferior entity recognition performance. To address this issue, we propose an effective span-based method, named SMNER, which achieves a more consistent multimodal alignment from the perspectives of information-theoretic and cross-modal interaction, respectively. Specifically, we first introduce a cross-modal information bottleneck module for the global-level multimodal alignment (whole image-whole text). This module aims to encourage the semantic distribution of the image to be closer to the semantic distribution of the text, which can enable the filtering out of visual noise. Next, we introduce a cross-modal attention module for the local-level multimodal alignment (regions-spans), which captures the correlations between regions in the image and spans in the text, enabling a more precise alignment of the two modalities. Extensive experiments conducted on two benchmark datasets demonstrate that SMNER outperforms the state-of-the-art baselines.

Original languageEnglish
Title of host publication2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings
EditorsNicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
PublisherEuropean Language Resources Association (ELRA)
Pages1063-1072
Number of pages10
ISBN (Electronic)9782493814104
Publication statusPublished - 2024
EventJoint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024 - Hybrid, Torino, Italy
Duration: 20 May 202425 May 2024

Publication series

Name2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings

Conference

ConferenceJoint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024
Country/TerritoryItaly
CityHybrid, Torino
Period20/05/2425/05/24

Keywords

  • Multimodal alignment
  • Multimodal fusion
  • Multimodal named entity recogniton

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