# Learning Logic Rules for Document-level Relation Extraction arXiv: https://arxiv.org/abs/2111.05407 ยท HTML: https://mlsanity.com/paper/2111.05407/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 39 references that are in the map. - First posted on arXiv 9 Nov 2021. ## Builds on (most cited first, 27 of 27) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019) - [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://mlsanity.com/paper/1809.10185/) (2018) - [Harnessing Deep Neural Networks with Logic Rules](https://mlsanity.com/paper/1603.06318/) (2016) - [RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2010.04029/) (2020) - [Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling](https://mlsanity.com/paper/2010.11304/) (2020) - [Cross-Sentence N-ary Relation Extraction with Graph LSTMs](https://mlsanity.com/paper/1708.03743/) (2017) - [Reasoning with Latent Structure Refinement for Document-Level Relation Extraction](https://mlsanity.com/paper/2005.06312/) (2020) - [Double Graph Based Reasoning for Document-level Relation Extraction](https://mlsanity.com/paper/2009.13752/) (2020) - [Coreferential Reasoning Learning for Language Representation](https://mlsanity.com/paper/2004.06870/) (2020) - [Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs](https://mlsanity.com/paper/1909.00228/) (2019) - [Probabilistic Logic Neural Networks for Reasoning](https://mlsanity.com/paper/1906.08495/) (2019) - [Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction](https://mlsanity.com/paper/2102.10249/) (2021) - [Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network](https://mlsanity.com/paper/1906.04684/) (2019) - [Efficient Probabilistic Logic Reasoning with Graph Neural Networks](https://mlsanity.com/paper/2001.11850/) (2020) - [Global-to-Local Neural Networks for Document-Level Relation Extraction](https://mlsanity.com/paper/2009.10359/) (2020) - [Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction](https://mlsanity.com/paper/1802.10569/) (2018) - [Fine-tune Bert for DocRED with Two-step Process](https://mlsanity.com/paper/1909.11898/) (2019) - [HIN: Hierarchical Inference Network for Document-Level Relation Extraction](https://mlsanity.com/paper/2003.12754/) (2020) - [UniRE: A Unified Label Space for Entity Relation Extraction](https://mlsanity.com/paper/2107.04292/) (2021) - [Classifying Relations by Ranking with Convolutional Neural Networks](https://mlsanity.com/paper/1504.06580/) (2015) - [DWIE: an entity-centric dataset for multi-task document-level information extraction](https://mlsanity.com/paper/2009.12626/) (2020) - [Globally Normalized Transition-Based Neural Networks](https://mlsanity.com/paper/1603.06042/) (2016) - [Integrating Deep Learning with Logic Fusion for Information Extraction](https://mlsanity.com/paper/1912.03041/) (2019) - [Deep Probabilistic Logic: A Unifying Framework for Indirect Supervision](https://mlsanity.com/paper/1808.08485/) (2018) - [Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings](https://mlsanity.com/paper/1805.10586/) (2018) ## Built on by (most cited first, 15 of 15) - [Relation-Specific Attentions over Entity Mentions for Enhanced Document-Level Relation Extraction](https://mlsanity.com/paper/2205.14393/) (2022) - [MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation](https://mlsanity.com/paper/2311.09105/) (2023) - [RuAG: Learned-rule-augmented Generation for Large Language Models](https://mlsanity.com/paper/2411.03349/) (2024) - [Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning](https://mlsanity.com/paper/2201.04826/) (2022) - [Rethinking Document-Level Relation Extraction: A Reality Check](https://mlsanity.com/paper/2306.08953/) (2023) - [A Comprehensive Survey of Document-level Relation Extraction (2016-2023)](https://mlsanity.com/paper/2309.16396/) (2023) - [Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction](https://mlsanity.com/paper/2205.01909/) (2022) - [Document-level Relation Extraction with Relation Correlations](https://mlsanity.com/paper/2212.10171/) (2022) - [Federated Neuro-Symbolic Learning](https://mlsanity.com/paper/2308.15324/) (2023) - [Graph-Augmented Relation Extraction Model with LLMs-Generated Support Document](https://mlsanity.com/paper/2410.23452/) (2024) - [A systematic review of relation extraction task since the emergence of Transformers](https://mlsanity.com/paper/2511.03610/) (2025) - [Analyzing the Influence of Knowledge Graph Information on Relation Extraction](https://mlsanity.com/paper/2506.16343/) (2025) - [Multi-Relation Extraction in Entity Pairs using Global Context](https://mlsanity.com/paper/2507.22926/) (2025) - [CONSISTRE: A Unified Consistency-Aware Framework for Document-Level Relation Extraction with Large Language Models](https://mlsanity.com/paper/2607.24312/) (2026) - [Ontology-Driven Structural Regularization for Document-Level Relation Extraction](https://mlsanity.com/paper/2608.20856/) (2026) ## Abstract (arXiv) 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.