Papers › Learning Logic Rules for Document-level Relation Extraction

Learning Logic Rules for Document-level Relation Extraction

9 Nov 2021EMNLP 2021 11arXiv:2111.05407archive 2025-07-28

Dongyu Ru, Changzhi Sun, Jiangtao Feng, Lin Qiu, Hao Zhou, Weinan Zhang, Yong Yu, Lei LI

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 LogiRE significantly outperforms several strong baselines in terms of relation performance (1.8 F1 score) and logical consistency (over 3.3 logic score). Our code is available at https://github.com/rudongyu/LogiRE.

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append_chain rudongyu/logire/reader.py official repository unverified MIT (permissive) · c946309d3e0f1a17 · report
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Tasks

Document-level Relation ExtractionRelation Extraction

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Relation Extraction DocRED ATLOP + LogiRE F1 61.45 #21 of 62 Archive leaderboard report
Relation Extraction DocRED ATLOP + LogiRE Ign F1 59.48 #21 of 62 Archive leaderboard report
Relation Extraction DocRED GAIN + LogiRE F1 60.61 #29 of 62 Archive leaderboard report
Relation Extraction DocRED GAIN + LogiRE Ign F1 58.62 #29 of 62 Archive leaderboard report

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