Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning

Xiangrong Zeng, Shizhu He, Daojian Zeng, Kang Liu, Shengping Liu, Jun Zhao


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.
Anthology ID:
D19-1035
Volume:
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:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
367–377
URL:
https://www.aclweb.org/anthology/D19-1035
DOI:
10.18653/v1/D19-1035
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PDF:
https://www.aclweb.org/anthology/D19-1035.pdf