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

TL;DR

This paper presents a novel deep learning approach for relation extraction that jointly predicts dependency and semantic relations, with a new information flow control mechanism, leading to improved cross-domain performance.

Contribution

It introduces a joint dependency and semantic relation prediction model with an information flow control mechanism to enhance cross-domain relation extraction.

Findings

Outperforms existing methods on benchmark datasets

Improves cross-domain generalization in relation extraction

Effectively utilizes dependency trees with controlled information flow

Abstract

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.