Event Presence Prediction Helps Trigger Detection Across Languages

• URL: http://arxiv.org/abs/2009.07188v1
• Date: Tue, 15 Sep 2020 15:52:21 GMT
• Title: Event Presence Prediction Helps Trigger Detection Across Languages
• Authors: Parul Awasthy and Tahira Naseem and Jian Ni and Taesun Moon and Radu Florian
• Abstract summary: 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.
• Score: 13.06818350795583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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