Year: 2021
Type: conference-paper
Abstract: Identifying events and mapping them to a pre-defined taxonomy of event types has long been an important NLP problem.Most previous work has relied heavily on labor-intensive, domain-specific, annotation, ignoring the semantic meaning of the event types' labels.Consequently, the learned models cannot effectively generalize to new label taxonomies and domains.We propose a zero-shot event extraction approach, which first identifies events with existing tools (e.g., SRL) and then maps them to a given taxonomy of event types in a zero-shot manner.Specifically, we leverage label representations induced by pre-trained language models, and map identified events to the target types via representation similarity.To semantically type the events' arguments, we further use the definition of the events (e.g., argument of type "Victim" appears as the argument of event of type "Attack") as global constraints to regularize the prediction.The proposed approach is shown to be very effective on the ACE-2005 dataset, which has 33 trigger and 22 argument types.Without using any annotation, we successfully map 83% of the triggers and 54% of the arguments to the semantic correct types, almost doubling the performance of previous zero-shot approaches 1 .
Authors: Hongming Zhang, Haoyu Wang, Dan Roth
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G1472423617, G4713059963, G638997368
OpenAlex ID: W3177112239