Deep Structured Neural Network for Event Temporal Relation Extraction

TL;DR

This paper introduces a deep structured neural network combining RNNs and SSVMs for improved event temporal relation extraction, leveraging long-term context and domain knowledge to outperform existing methods.

Contribution

The paper presents a novel deep structured learning framework that jointly trains neural networks and structured SVMs for better temporal relation extraction.

Findings

Achieves state-of-the-art performance on three datasets

Effectively incorporates domain knowledge as constraints

Demonstrates robustness with pre-trained contextual embeddings

Abstract

We propose a novel deep structured learning framework for event temporal relation extraction. The model consists of 1) a recurrent neural network (RNN) to learn scoring functions for pair-wise relations, and 2) a structured support vector machine (SSVM) to make joint predictions. The neural network automatically learns representations that account for long-term contexts to provide robust features for the structured model, while the SSVM incorporates domain knowledge such as transitive closure of temporal relations as constraints to make better globally consistent decisions. By jointly training the two components, our model combines the benefits of both data-driven learning and knowledge exploitation. Experimental results on three high-quality event temporal relation datasets (TCR, MATRES, and TB-Dense) demonstrate that incorporated with pre-trained contextualized embeddings, the proposed model achieves significantly better performances than the state-of-the-art methods on all three datasets. We also provide thorough ablation studies to investigate our model.