@inproceedings{beltagy-etal-2019-combining,
title = "Combining Distant and Direct Supervision for Neural Relation Extraction",
author = "Beltagy, Iz and
Lo, Kyle and
Ammar, Waleed",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1184",
doi = "10.18653/v1/N19-1184",
pages = "1858--1867",
abstract = "In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to sentences that are likely to express the relations. We improve such models by combining the distant supervision data with an additional directly-supervised data, which we use as supervision for the attention weights. We find that joint training on both types of supervision leads to a better model because it improves the model{'}s ability to identify noisy sentences. In addition, we find that sigmoidal attention weights with max pooling achieves better performance over the commonly used weighted average attention in this setup. Our proposed method achieves a new state-of-the-art result on the widely used FB-NYT dataset.",
}
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<abstract>In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to sentences that are likely to express the relations. We improve such models by combining the distant supervision data with an additional directly-supervised data, which we use as supervision for the attention weights. We find that joint training on both types of supervision leads to a better model because it improves the model’s ability to identify noisy sentences. In addition, we find that sigmoidal attention weights with max pooling achieves better performance over the commonly used weighted average attention in this setup. Our proposed method achieves a new state-of-the-art result on the widely used FB-NYT dataset.</abstract>
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%0 Conference Proceedings
%T Combining Distant and Direct Supervision for Neural Relation Extraction
%A Beltagy, Iz
%A Lo, Kyle
%A Ammar, Waleed
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F beltagy-etal-2019-combining
%X In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to sentences that are likely to express the relations. We improve such models by combining the distant supervision data with an additional directly-supervised data, which we use as supervision for the attention weights. We find that joint training on both types of supervision leads to a better model because it improves the model’s ability to identify noisy sentences. In addition, we find that sigmoidal attention weights with max pooling achieves better performance over the commonly used weighted average attention in this setup. Our proposed method achieves a new state-of-the-art result on the widely used FB-NYT dataset.
%R 10.18653/v1/N19-1184
%U https://aclanthology.org/N19-1184
%U https://doi.org/10.18653/v1/N19-1184
%P 1858-1867
Markdown (Informal)
[Combining Distant and Direct Supervision for Neural Relation Extraction](https://aclanthology.org/N19-1184) (Beltagy et al., NAACL 2019)
ACL
- Iz Beltagy, Kyle Lo, and Waleed Ammar. 2019. Combining Distant and Direct Supervision for Neural Relation Extraction. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1858–1867, Minneapolis, Minnesota. Association for Computational Linguistics.