@inproceedings{xiao-etal-2020-denoising,
title = "Denoising Relation Extraction from Document-level Distant Supervision",
author = "Xiao, Chaojun and
Yao, Yuan and
Xie, Ruobing and
Han, Xu and
Liu, Zhiyuan and
Sun, Maosong and
Lin, Fen and
Lin, Leyu",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.300",
doi = "10.18653/v1/2020.emnlp-main.300",
pages = "3683--3688",
abstract = "Distant supervision (DS) has been widely adopted to generate auto-labeled data for sentence-level relation extraction (RE) and achieved great results. However, the existing success of DS cannot be directly transferred to more challenging document-level relation extraction (DocRE), as the inevitable noise caused by DS may be even multiplied in documents and significantly harm the performance of RE. To alleviate this issue, we propose a novel pre-trained model for DocRE, which de-emphasize noisy DS data via multiple pre-training tasks. The experimental results on the large-scale DocRE benchmark show that our model can capture useful information from noisy data and achieve promising results.",
}
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<abstract>Distant supervision (DS) has been widely adopted to generate auto-labeled data for sentence-level relation extraction (RE) and achieved great results. However, the existing success of DS cannot be directly transferred to more challenging document-level relation extraction (DocRE), as the inevitable noise caused by DS may be even multiplied in documents and significantly harm the performance of RE. To alleviate this issue, we propose a novel pre-trained model for DocRE, which de-emphasize noisy DS data via multiple pre-training tasks. The experimental results on the large-scale DocRE benchmark show that our model can capture useful information from noisy data and achieve promising results.</abstract>
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%0 Conference Proceedings
%T Denoising Relation Extraction from Document-level Distant Supervision
%A Xiao, Chaojun
%A Yao, Yuan
%A Xie, Ruobing
%A Han, Xu
%A Liu, Zhiyuan
%A Sun, Maosong
%A Lin, Fen
%A Lin, Leyu
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F xiao-etal-2020-denoising
%X Distant supervision (DS) has been widely adopted to generate auto-labeled data for sentence-level relation extraction (RE) and achieved great results. However, the existing success of DS cannot be directly transferred to more challenging document-level relation extraction (DocRE), as the inevitable noise caused by DS may be even multiplied in documents and significantly harm the performance of RE. To alleviate this issue, we propose a novel pre-trained model for DocRE, which de-emphasize noisy DS data via multiple pre-training tasks. The experimental results on the large-scale DocRE benchmark show that our model can capture useful information from noisy data and achieve promising results.
%R 10.18653/v1/2020.emnlp-main.300
%U https://aclanthology.org/2020.emnlp-main.300
%U https://doi.org/10.18653/v1/2020.emnlp-main.300
%P 3683-3688
Markdown (Informal)
[Denoising Relation Extraction from Document-level Distant Supervision](https://aclanthology.org/2020.emnlp-main.300) (Xiao et al., EMNLP 2020)
ACL