  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.
