UMIE: Unified Multimodal Information Extraction with Instruction Tuning

• URL: http://arxiv.org/abs/2401.03082v1
• Date: Fri, 5 Jan 2024 22:52:15 GMT
• Title: UMIE: Unified Multimodal Information Extraction with Instruction Tuning
• Authors: Lin Sun, Kai Zhang, Qingyuan Li, Renze Lou
• Abstract summary: We propose UMIE, a unified multimodal information extractor, to unify three MIE tasks as a generation problem using instruction tuning. Extensive experiments show that our single UMIE outperforms various state-of-the-art (SoTA) methods across six MIE datasets on three tasks. Our research serves as an initial step towards a unified MIE model and initiates the exploration into both instruction tuning and large language models within the MIE domain.
• Score: 12.777967562175437
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal information extraction (MIE) gains significant attention as the popularity of multimedia content increases. However, current MIE methods often resort to using task-specific model structures, which results in limited generalizability across tasks and underutilizes shared knowledge across MIE tasks. To address these issues, we propose UMIE, a unified multimodal information extractor to unify three MIE tasks as a generation problem using instruction tuning, being able to effectively extract both textual and visual mentions. Extensive experiments show that our single UMIE outperforms various state-of-the-art (SoTA) methods across six MIE datasets on three tasks. Furthermore, in-depth analysis demonstrates UMIE's strong generalization in the zero-shot setting, robustness to instruction variants, and interpretability. Our research serves as an initial step towards a unified MIE model and initiates the exploration into both instruction tuning and large language models within the MIE domain. Our code, data, and model are available at https://github.com/ZUCC-AI/UMIE

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