@inproceedings{li-etal-2020-active,
title = "Active Testing: An Unbiased Evaluation Method for Distantly Supervised Relation Extraction",
author = "Li, Pengshuai and
Zhang, Xinsong and
Jia, Weijia and
Zhao, Wei",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.20",
doi = "10.18653/v1/2020.findings-emnlp.20",
pages = "204--211",
abstract = "Distant supervision has been a widely used method for neural relation extraction for its convenience of automatically labeling datasets. However, existing works on distantly supervised relation extraction suffer from the low quality of test set, which leads to considerable biased performance evaluation. These biases not only result in unfair evaluations but also mislead the optimization of neural relation extraction. To mitigate this problem, we propose a novel evaluation method named active testing through utilizing both the noisy test set and a few manual annotations. Experiments on a widely used benchmark show that our proposed approach can yield approximately unbiased evaluations for distantly supervised relation extractors.",
}
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<abstract>Distant supervision has been a widely used method for neural relation extraction for its convenience of automatically labeling datasets. However, existing works on distantly supervised relation extraction suffer from the low quality of test set, which leads to considerable biased performance evaluation. These biases not only result in unfair evaluations but also mislead the optimization of neural relation extraction. To mitigate this problem, we propose a novel evaluation method named active testing through utilizing both the noisy test set and a few manual annotations. Experiments on a widely used benchmark show that our proposed approach can yield approximately unbiased evaluations for distantly supervised relation extractors.</abstract>
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%0 Conference Proceedings
%T Active Testing: An Unbiased Evaluation Method for Distantly Supervised Relation Extraction
%A Li, Pengshuai
%A Zhang, Xinsong
%A Jia, Weijia
%A Zhao, Wei
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-active
%X Distant supervision has been a widely used method for neural relation extraction for its convenience of automatically labeling datasets. However, existing works on distantly supervised relation extraction suffer from the low quality of test set, which leads to considerable biased performance evaluation. These biases not only result in unfair evaluations but also mislead the optimization of neural relation extraction. To mitigate this problem, we propose a novel evaluation method named active testing through utilizing both the noisy test set and a few manual annotations. Experiments on a widely used benchmark show that our proposed approach can yield approximately unbiased evaluations for distantly supervised relation extractors.
%R 10.18653/v1/2020.findings-emnlp.20
%U https://aclanthology.org/2020.findings-emnlp.20
%U https://doi.org/10.18653/v1/2020.findings-emnlp.20
%P 204-211
Markdown (Informal)
[Active Testing: An Unbiased Evaluation Method for Distantly Supervised Relation Extraction](https://aclanthology.org/2020.findings-emnlp.20) (Li et al., Findings 2020)
ACL