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In this paper, we approach a recent and under-researched paradigm for the task of event detection (ED) by casting it as a question-answering (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.
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Few-shot learning, [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, Event detection, Question answering, Few-shot learning., [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 004, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-DL]Computer Science [cs]/Digital Libraries [cs.DL], Question answering, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Event detection, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-DL] Computer Science [cs]/Digital Libraries [cs.DL], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC]
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Few-shot learning, [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, Event detection, Question answering, Few-shot learning., [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 004, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-DL]Computer Science [cs]/Digital Libraries [cs.DL], Question answering, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Event detection, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-DL] Computer Science [cs]/Digital Libraries [cs.DL], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC]
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