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        <title>Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions</title>
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    <abstract>This paper presents a neural relation extraction method to deal with the noisy training data generated by distant supervision. Previous studies mainly focus on sentence-level de-noising by designing neural networks with intra-bag attentions. In this paper, both intra-bag and inter-bag attentions are considered in order to deal with the noise at sentence-level and bag-level respectively. First, relation-aware bag representations are calculated by weighting sentence embeddings using intra-bag attentions. Here, each possible relation is utilized as the query for attention calculation instead of only using the target relation in conventional methods. Furthermore, the representation of a group of bags in the training set which share the same relation label is calculated by weighting bag representations using a similarity-based inter-bag attention module. Finally, a bag group is utilized as a training sample when building our relation extractor. Experimental results on the New York Times dataset demonstrate the effectiveness of our proposed intra-bag and inter-bag attention modules. Our method also achieves better relation extraction accuracy than state-of-the-art methods on this dataset.</abstract>
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%0 Conference Proceedings
%T Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions
%A Ye, Zhi-Xiu
%A Ling, Zhen-Hua
%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 ye-ling-2019-distant
%X This paper presents a neural relation extraction method to deal with the noisy training data generated by distant supervision. Previous studies mainly focus on sentence-level de-noising by designing neural networks with intra-bag attentions. In this paper, both intra-bag and inter-bag attentions are considered in order to deal with the noise at sentence-level and bag-level respectively. First, relation-aware bag representations are calculated by weighting sentence embeddings using intra-bag attentions. Here, each possible relation is utilized as the query for attention calculation instead of only using the target relation in conventional methods. Furthermore, the representation of a group of bags in the training set which share the same relation label is calculated by weighting bag representations using a similarity-based inter-bag attention module. Finally, a bag group is utilized as a training sample when building our relation extractor. Experimental results on the New York Times dataset demonstrate the effectiveness of our proposed intra-bag and inter-bag attention modules. Our method also achieves better relation extraction accuracy than state-of-the-art methods on this dataset.
%R 10.18653/v1/N19-1288
%U https://aclanthology.org/N19-1288
%U https://doi.org/10.18653/v1/N19-1288
%P 2810-2819

Markdown (Informal)

[Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions](https://aclanthology.org/N19-1288) (Ye & Ling, NAACL 2019)

• Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions (Ye & Ling, NAACL 2019)

ACL

• Zhi-Xiu Ye and Zhen-Hua Ling. 2019. Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions. 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 2810–2819, Minneapolis, Minnesota. Association for Computational Linguistics.