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Relation Extraction (RE) is one of the fundamental tasks in InformationExtraction and Natural Language Processing. Dependency trees have been shown tobe a very useful source of information for this task. The current deep learningmodels for relation extraction has mainly exploited this dependency informationby guiding their computation along the structures of the dependency trees. Onepotential problem with this approach is it might prevent the models fromcapturing important context information beyond syntactic structures and causethe poor cross-domain generalization. This paper introduces a novel method touse dependency trees in RE for deep learning models that jointly predictsdependency and semantics relations. We also propose a new mechanism to controlthe information flow in the model based on the input entity mentions. Ourextensive experiments on benchmark datasets show that the proposed modeloutperforms the existing methods for RE significantly.

Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control