None Class Ranking Loss for Document-Level Relation Extraction

• URL: http://arxiv.org/abs/2205.00476v2
• Date: Tue, 3 May 2022 05:27:54 GMT
• Title: None Class Ranking Loss for Document-Level Relation Extraction
• Authors: Yang Zhou and Wee Sun Lee
• Abstract summary: Document-level relation extraction (RE) aims at extracting relations among entities expressed across multiple sentences. In a typical document, most entity pairs do not express any pre-defined relation and are labeled as "none" or "no relation"
• Score: 22.173080823450498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Document-level relation extraction (RE) aims at extracting relations among entities expressed across multiple sentences, which can be viewed as a multi-label classification problem. In a typical document, most entity pairs do not express any pre-defined relation and are labeled as "none" or "no relation". For good document-level RE performance, it is crucial to distinguish such none class instances (entity pairs) from those of pre-defined classes (relations). However, most existing methods only estimate the probability of pre-defined relations independently without considering the probability of "no relation". This ignores the context of entity pairs and the label correlations between the none class and pre-defined classes, leading to sub-optimal predictions. To address this problem, we propose a new multi-label loss that encourages large margins of label confidence scores between each pre-defined class and the none class, which enables captured label correlations and context-dependent thresholding for label prediction. To gain further robustness against positive-negative imbalance and mislabeled data that could appear in real-world RE datasets, we propose a margin regularization and a margin shifting technique. Experimental results demonstrate that our method significantly outperforms existing multi-label losses for document-level RE and works well in other multi-label tasks such as emotion classification when none class instances are available for training.

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