Semi-supervised New Event Type Induction and Event Detection

Lifu Huang, Heng Ji


Abstract
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.
Anthology ID:
2020.emnlp-main.53
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
718–724
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.53
DOI:
10.18653/v1/2020.emnlp-main.53
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.53.pdf