Event factuality identification via generative adversarial networks with auxiliary classification

Abstract

Event factuality identification is an important semantic task in NLP. Traditional research heavily relies on annotated texts. This paper proposes a twostep framework, first extracting essential factors related with event factuality from raw texts as the input, and then identifying the factuality of events via a Generative Adversarial Network with Auxiliary Classification (AC-GAN). The use of AC-GAN allows the model to learn more syntactic information and address the imbalance among factuality values. Experimental results on FactBank show that our method significantly outperforms several stateof-the-art baselines, particularly on events with embedded sources, speculative and negative factuality values.