Event Factuality Identification via Generative Adversarial Networks with Auxiliary Classification

Event Factuality Identification via Generative Adversarial Networks with Auxiliary Classification

Zhong Qian, Peifeng Li, Yue Zhang, Guodong Zhou, Qiaoming Zhu

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 4293-4300. https://doi.org/10.24963/ijcai.2018/597

Event factuality identification is an important semantic task in NLP. Traditional research heavily relies on annotated texts. This paper proposes a two-step 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 state-of-the-art baselines, particularly on events with embedded sources, speculative and negative factuality values.
Keywords:
Natural Language Processing: Information Extraction
Natural Language Processing: Natural Language Processing