@inproceedings{zeng-etal-2019-learning,
title = "Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning",
author = "Zeng, Xiangrong and
He, Shizhu and
Zeng, Daojian and
Liu, Kang and
Liu, Shengping and
Zhao, Jun",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1035",
doi = "10.18653/v1/D19-1035",
pages = "367--377",
abstract = "The multiple relation extraction task tries to extract all relational facts from a sentence. Existing works didn{'}t consider the extraction order of relational facts in a sentence. In this paper we argue that the extraction order is important in this task. To take the extraction order into consideration, we apply the reinforcement learning into a sequence-to-sequence model. The proposed model could generate relational facts freely. Widely conducted experiments on two public datasets demonstrate the efficacy of the proposed method.",
}
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<abstract>The multiple relation extraction task tries to extract all relational facts from a sentence. Existing works didn’t consider the extraction order of relational facts in a sentence. In this paper we argue that the extraction order is important in this task. To take the extraction order into consideration, we apply the reinforcement learning into a sequence-to-sequence model. The proposed model could generate relational facts freely. Widely conducted experiments on two public datasets demonstrate the efficacy of the proposed method.</abstract>
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%0 Conference Proceedings
%T Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning
%A Zeng, Xiangrong
%A He, Shizhu
%A Zeng, Daojian
%A Liu, Kang
%A Liu, Shengping
%A Zhao, Jun
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F zeng-etal-2019-learning
%X The multiple relation extraction task tries to extract all relational facts from a sentence. Existing works didn’t consider the extraction order of relational facts in a sentence. In this paper we argue that the extraction order is important in this task. To take the extraction order into consideration, we apply the reinforcement learning into a sequence-to-sequence model. The proposed model could generate relational facts freely. Widely conducted experiments on two public datasets demonstrate the efficacy of the proposed method.
%R 10.18653/v1/D19-1035
%U https://aclanthology.org/D19-1035
%U https://doi.org/10.18653/v1/D19-1035
%P 367-377
Markdown (Informal)
[Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning](https://aclanthology.org/D19-1035) (Zeng et al., EMNLP-IJCNLP 2019)
ACL