Abstract
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.
Keywords
- Event detection
- Question answering
- Few-shot learning
This work has been supported by the European Union’s Horizon 2020 research and innovation program under grants 770299 (NewsEye) and 825153 (Embeddia), and by the ANNA and Termitrad projects funded by the Nouvelle-Aquitaine Region.
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- 1.
- 2.
In one view, the recent tasks titled MRC can also be seen as the extended tasks of question answering (QA).
- 3.
We note here that event extraction generally depends on previous phases as, for example, named entity recognition, entity mention coreference, and classification. Thereinto, the named entity recognition is another hard task in the ACE evaluation and not the focus of this paper. Therefore, we will temporarily skip the phase and instead directly use the entities provided by ACE, following previous work [4, 7, 10, 12, 14, 15].
- 4.
SQuAD v1.1 consists of reference passages from Wikipedia with answers and questions constructed by annotators after viewing the passage.
- 5.
SQuADv2.0 augmented the SQuAD v1.1 collection with additional questions that did not have answers in the referenced passage.
- 6.
- 7.
- 8.
The code is available at https://github.com/nlpcl-lab/ace2005-preprocessing as it consists of the same pre-processing as utilized in several other papers [21, 23].
- 9.
The sentence is lowercased for the uncased models.
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Boros, E., Moreno, J.G., Doucet, A. (2022). Exploring Entities in Event Detection as Question Answering. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13185. Springer, Cham. https://doi.org/10.1007/978-3-030-99736-6_5
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