Semi-supervised New Event Type Induction and Event Detection
Lifu Huang, Heng Ji · 2020
Most previous event extraction studies assume a set of target event types and corresponding event annotations are given, which could be very expensive.In this paper, we work on a new task of semi-supervised event type induction, aiming to automatically discover a set of unseen types from a given corpus by leveraging annotations available for a few seen types.We design a Semi-Supervised Vector Quantized Variational Autoencoder framework to automatically learn a discrete latent type representation for each seen and unseen type and optimize them using seen type event annotations.A variational autoencoder is further introduced to enforce the reconstruction of each event mention conditioned on its latent type distribution.Experiments show that our approach can not only achieve state-of-the-art performance on supervised event detection but also discover high-quality new event types. 1