  Relation Extraction (RE) is one of the fundamental tasks in Information
Extraction and Natural Language Processing. Dependency trees have been shown to
be a very useful source of information for this task. The current deep learning
models for relation extraction has mainly exploited this dependency information
by guiding their computation along the structures of the dependency trees. One
potential problem with this approach is it might prevent the models from
capturing important context information beyond syntactic structures and cause
the poor cross-domain generalization. This paper introduces a novel method to
use dependency trees in RE for deep learning models that jointly predicts
dependency and semantics relations. We also propose a new mechanism to control
the information flow in the model based on the input entity mentions. Our
extensive experiments on benchmark datasets show that the proposed model
outperforms the existing methods for RE significantly.
