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Event Presence Prediction Helps Trigger Detection Across Languages

15 September 2020
Parul Awasthy
Tahira Naseem
Jian Ni
Taesun Moon
Radu Florian
ArXiv (abs)PDFHTML
Abstract

The task of event detection and classification is central to most information retrieval applications. We show that a Transformer based architecture can effectively model event extraction as a sequence labeling task. We propose a combination of sentence level and token level training objectives that significantly boosts the performance of a BERT based event extraction model. Our approach achieves a new state-of-the-art performance on ACE 2005 data for English and Chinese. We also test our model on ERE Spanish, achieving an average gain of 2 absolute F1 points over prior best performing model.

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