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.

Highlights

  • Events are often presented in sentences via the indication of anchor/trigger words (Nguyen et al, 2016a)

  • We focus on the recent regression formulation of Event factuality prediction (EFP) that aims to predict a real score in the range of [-3,+3] to quantify the occurrence possibility of a given event mention (Stanovsky et al, 2017; Rudinger et al, 2018)

  • We propose a novel method to integrate syntactic and semantic structures of the sentences based on the graph convolutional neural networks (GCN) (Kipf and Welling, 2016) for EFP

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Summary

Introduction

Events are often presented in sentences via the indication of anchor/trigger words (i.e., the main words to evoke the events, called event mentions) (Nguyen et al, 2016a). The dependency tree of the sentence “I will, after seeing the treatment of others, go back when I need medical care.” will be helpful to directly link the anchor word “go” to the modal auxiliary “will” to successfully predict the non-factuality of the event mention. While deep learning models with the sequential structure can help to downgrade the noisy words (i.e., “back”) based on the semantic importance and the close distance with “go”, these models will struggle to capture “will” for the factuality of “go” due to their long distance From this example, we see that the syntactic and semantic information can complement each other to both promote the important context words and blur the irrelevant words. The extensive experiments show that the proposed model is very effective for EFP

Related Work
Sentence Encoding
Structure Induction
Prediction
Experiments
Comparing to the State of the Art
Ablation Study
Findings
Conclusion & Future Work
Full Text
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