Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction

Qian Li, Shu Guo, Cheng Ji, Xutan Peng, Shiyao Cui, Jianxin Li, Lihong Wang

Abstract
Multi-Modal Relation Extraction (MMRE) aims at identifying the relation between two entities in texts that contain visual clues. Rich visual content is valuable for the MMRE task, but existing works cannot well model finer associations among different modalities, failing to capture the truly helpful visual information and thus limiting relation extraction performance. In this paper, we propose a novel MMRE framework to better capture the deeper correlations of text, entity pair, and image/objects, so as to mine more helpful information for the task, termed as DGF-PT. We first propose a prompt-based autoregressive encoder, which builds the associations of intra-modal and inter-modal features related to the task, respectively by entity-oriented and object-oriented prefixes. To better integrate helpful visual information, we design a dual-gated fusion module to distinguish the importance of image/objects and further enrich text representations. In addition, a generative decoder is introduced with entity type restriction on relations, better filtering out candidates. Extensive experiments conducted on the benchmark dataset show that our approach achieves excellent performance compared to strong competitors, even in the few-shot situation.
Anthology ID:
2023.findings-acl.572
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8982–8994
Language:
URL:
https://aclanthology.org/2023.findings-acl.572
DOI:
10.18653/v1/2023.findings-acl.572
Bibkey:
Cite (ACL):
Qian Li, Shu Guo, Cheng Ji, Xutan Peng, Shiyao Cui, Jianxin Li, and Lihong Wang. 2023. Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction. In Findings of the Association for Computational Linguistics: ACL 2023, pages 8982–8994, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction (Li et al., Findings 2023)
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https://preview.aclanthology.org/emnlp22-frontmatter/2023.findings-acl.572.pdf
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