@inproceedings{ru-etal-2021-learning,
title = "Learning Logic Rules for Document-Level Relation Extraction",
author = "Ru, Dongyu and
Sun, Changzhi and
Feng, Jiangtao and
Qiu, Lin and
Zhou, Hao and
Zhang, Weinan and
Yu, Yong and
Li, Lei",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.95",
doi = "10.18653/v1/2021.emnlp-main.95",
pages = "1239--1250",
abstract = "Document-level relation extraction aims to identify relations between entities in a whole document. Prior efforts to capture long-range dependencies have relied heavily on implicitly powerful representations learned through (graph) neural networks, which makes the model less transparent. To tackle this challenge, in this paper, we propose LogiRE, a novel probabilistic model for document-level relation extraction by learning logic rules. LogiRE treats logic rules as latent variables and consists of two modules: a rule generator and a relation extractor. The rule generator is to generate logic rules potentially contributing to final predictions, and the relation extractor outputs final predictions based on the generated logic rules. Those two modules can be efficiently optimized with the expectation-maximization (EM) algorithm. By introducing logic rules into neural networks, LogiRE can explicitly capture long-range dependencies as well as enjoy better interpretation. Empirical results show that significantly outperforms several strong baselines in terms of relation performance and logical consistency. Our code is available at \url{https://github.com/rudongyu/LogiRE}.",
}
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<abstract>Document-level relation extraction aims to identify relations between entities in a whole document. Prior efforts to capture long-range dependencies have relied heavily on implicitly powerful representations learned through (graph) neural networks, which makes the model less transparent. To tackle this challenge, in this paper, we propose LogiRE, a novel probabilistic model for document-level relation extraction by learning logic rules. LogiRE treats logic rules as latent variables and consists of two modules: a rule generator and a relation extractor. The rule generator is to generate logic rules potentially contributing to final predictions, and the relation extractor outputs final predictions based on the generated logic rules. Those two modules can be efficiently optimized with the expectation-maximization (EM) algorithm. By introducing logic rules into neural networks, LogiRE can explicitly capture long-range dependencies as well as enjoy better interpretation. Empirical results show that significantly outperforms several strong baselines in terms of relation performance and logical consistency. Our code is available at https://github.com/rudongyu/LogiRE.</abstract>
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%0 Conference Proceedings
%T Learning Logic Rules for Document-Level Relation Extraction
%A Ru, Dongyu
%A Sun, Changzhi
%A Feng, Jiangtao
%A Qiu, Lin
%A Zhou, Hao
%A Zhang, Weinan
%A Yu, Yong
%A Li, Lei
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F ru-etal-2021-learning
%X Document-level relation extraction aims to identify relations between entities in a whole document. Prior efforts to capture long-range dependencies have relied heavily on implicitly powerful representations learned through (graph) neural networks, which makes the model less transparent. To tackle this challenge, in this paper, we propose LogiRE, a novel probabilistic model for document-level relation extraction by learning logic rules. LogiRE treats logic rules as latent variables and consists of two modules: a rule generator and a relation extractor. The rule generator is to generate logic rules potentially contributing to final predictions, and the relation extractor outputs final predictions based on the generated logic rules. Those two modules can be efficiently optimized with the expectation-maximization (EM) algorithm. By introducing logic rules into neural networks, LogiRE can explicitly capture long-range dependencies as well as enjoy better interpretation. Empirical results show that significantly outperforms several strong baselines in terms of relation performance and logical consistency. Our code is available at https://github.com/rudongyu/LogiRE.
%R 10.18653/v1/2021.emnlp-main.95
%U https://aclanthology.org/2021.emnlp-main.95
%U https://doi.org/10.18653/v1/2021.emnlp-main.95
%P 1239-1250
Markdown (Informal)
[Learning Logic Rules for Document-Level Relation Extraction](https://aclanthology.org/2021.emnlp-main.95) (Ru et al., EMNLP 2021)
ACL
- Dongyu Ru, Changzhi Sun, Jiangtao Feng, Lin Qiu, Hao Zhou, Weinan Zhang, Yong Yu, and Lei Li. 2021. Learning Logic Rules for Document-Level Relation Extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1239–1250, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.