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arXiv · 2011.03888

Denoising Relation Extraction from Document-level Distant Supervision

Chaojun Xiao·Yuan Yao·Ruobing Xie·Xu Han·Zhiyuan Liu·Maosong Sun·Fen Lin·Leyu Lin
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Abstract

Distant supervision (DS) has been widely used to generate auto-labeled data for sentence-level relation extraction (RE), which improves RE performance. However, the existing success of DS cannot be directly transferred to the more challenging document-level relation extraction (DocRE), since the inherent noise in DS may be even multiplied in document level and significantly harm the performance of RE. To address this challenge, we propose a novel pre-trained model for DocRE, which denoises the document-level DS data via multiple pre-training tasks. Experimental results on the large-scale DocRE benchmark show that our model can capture useful information from noisy DS data and achieve promising results.

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Chaojun Xiao, Yuan Yao, Ruobing Xie, Xu Han, Zhiyuan Liu, Maosong Sun, Fen Lin, Leyu Lin. 2020-11-08. Denoising Relation Extraction from Document-level Distant Supervision. https://arxiv.org/abs/2011.03888

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