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Exploring Entities in Event Detection as Question Answering

Abstract : In this paper, we approach a recent and under-researched paradigm for the task of event detection (ED) by casting it as a questionanswering (QA) problem with the possibility of multiple answers and the support of entities. The extraction of event triggers is, thus, transformed into the task of identifying answer spans from a context, while also focusing on the surrounding entities. The architecture is based on a pre-trained and fine-tuned language model, where the input context is augmented with entities marked at different levels, their positions, their types, and, finally, their argument roles. Experiments on the ACE 2005 corpus demonstrate that the proposed model properly leverages entity information in detecting events and that it is a viable solution for the ED task. Moreover, we demonstrate that our method with different entity markers is particularly able to extract unseen event types in few-shot learning settings.
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Contributor : Antoine Doucet Connect in order to contact the contributor
Submitted on : Friday, April 8, 2022 - 8:30:09 PM
Last modification on : Monday, July 4, 2022 - 8:49:07 AM
Long-term archiving on: : Saturday, July 9, 2022 - 7:22:21 PM


ECIR2022-Exploring Entities in...
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Emanuela Boros, José G. Moreno, Antoine Doucet. Exploring Entities in Event Detection as Question Answering. Matthias Hagen; Suzan Verberne; Craig Macdonald; Christin Seifert; Krisztian Balog; Kjetil Nørvåg; Vinay Setty. Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part I, 13185 (Part 1), Springer International Publishing, pp.65-79, 2022, Lecture Notes in Computer Science book series (LNCS), 978-3-030-99735-9. ⟨10.1007/978-3-030-99736-6_5⟩. ⟨hal-03635982⟩



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