1. Exploring Entities in Event Detection as Question Answering
- Author
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Boros, Emanuela, Moreno, Jose G., Doucet, Antoine, Laboratoire Informatique, Image et Interaction - EA 2118 (L3I), La Rochelle Université (ULR), Recherche d’Information et Synthèse d’Information (IRIT-IRIS), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Région Nouvelle-Aquitaine : ANNA and Termitrad projects, Matthias Hagen, Suzan Verberne, Craig Macdonald, Christin Seifert, Krisztian Balog, Kjetil Nørvåg, Vinay Setty, European Project: 770299,NewsEye, and European Project: 825153 ,EMBADDIA
- Subjects
[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,Few-shot learning ,[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] ,Event detection, Question answering, Few-shot learning ,Question answering ,[INFO.INFO-DL]Computer Science [cs]/Digital Libraries [cs.DL] ,Event detection ,[INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC] ,[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL] ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] - Abstract
International audience; 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.
- Published
- 2022
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