Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures

TL;DR

This paper introduces a novel graph-based neural network that effectively integrates syntactic and semantic information for event factuality prediction, improving the understanding of whether events in sentences have occurred.

Contribution

The work presents a new graph neural network model that better combines syntactic and semantic structures for event factuality prediction.

Findings

The proposed model outperforms previous methods on EFP tasks.

Graph-based integration of syntactic and semantic info improves prediction accuracy.

The approach demonstrates the importance of structured information in language understanding.

Abstract

Event factuality prediction (EFP) is the task of assessing the degree to which an event mentioned in a sentence has happened. For this task, both syntactic and semantic information are crucial to identify the important context words. The previous work for EFP has only combined these information in a simple way that cannot fully exploit their coordination. In this work, we introduce a novel graph-based neural network for EFP that can integrate the semantic and syntactic information more effectively. Our experiments demonstrate the advantage of the proposed model for EFP.