Label Drop for Multi-Aspect Relation Modeling in Universal Information Extraction

• URL: http://arxiv.org/abs/2502.12614v1
• Date: Tue, 18 Feb 2025 07:53:26 GMT
• Title: Label Drop for Multi-Aspect Relation Modeling in Universal Information Extraction
• Authors: Lu Yang, Jiajia Li, En Ci, Lefei Zhang, Zuchao Li, Ping Wang,
• Abstract summary: Universal Information Extraction (UIE) has garnered significant attention due to its ability to address model explosion problems effectively. We propose LDNet, which incorporates multi-aspect relation modeling and a label drop mechanism. Experiments show that LDNet outperforms or achieves competitive performance with state-of-the-art systems on 9 tasks, 33 datasets.
• Score: 39.820981637594016
• License:
• Abstract: Universal Information Extraction (UIE) has garnered significant attention due to its ability to address model explosion problems effectively. Extractive UIE can achieve strong performance using a relatively small model, making it widely adopted. Extractive UIEs generally rely on task instructions for different tasks, including single-target instructions and multiple-target instructions. Single-target instruction UIE enables the extraction of only one type of relation at a time, limiting its ability to model correlations between relations and thus restricting its capability to extract complex relations. While multiple-target instruction UIE allows for the extraction of multiple relations simultaneously, the inclusion of irrelevant relations introduces decision complexity and impacts extraction accuracy. Therefore, for multi-relation extraction, we propose LDNet, which incorporates multi-aspect relation modeling and a label drop mechanism. By assigning different relations to different levels for understanding and decision-making, we reduce decision confusion. Additionally, the label drop mechanism effectively mitigates the impact of irrelevant relations. Experiments show that LDNet outperforms or achieves competitive performance with state-of-the-art systems on 9 tasks, 33 datasets, in both single-modal and multi-modal, few-shot and zero-shot settings.\footnote{https://github.com/Lu-Yang666/LDNet}

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