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In this paper, we propose 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.
Matching the blanks: Distributional similarity for relation learning
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David Wadden, Ulme Wennberg, Yi Luan, and Hannaneh Hajishirzi. 2019 · 1909
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Exploiting argument information to improve event detection via supervised attention mechanisms
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Attention is all you need
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Jointly multiple events extraction via attention-based graph information aggregation
Xiao Liu, Zhunchen Luo, and Heyan Huang. 2018 · 2018
Cited alongside, same era.
Graph convolutional networks with argument-aware pooling for event detection
Thien Huu Nguyen and Ralph Grishman. 2018 · 2018
Cited alongside, same era.
On identifiability in transformers
Gino Brunner, Yang Liu, Damian Pascual, Oliver Richter, Massimiliano Ciaramita, and Roger Wattenhofer. 2019 · 2019
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Visualizing memorization in rnns
Andreas Madsen. 2019 · 2019
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Adversarial training for weakly supervised event detection
Xiaozhi Wang, Xu Han, Zhiyuan Liu, Maosong Sun, and Peng Li. 2019 · 2019
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Exploring pre-trained language models for event extraction and generation
Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, and Dongsheng Li. 2019 · 2019
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Event extraction by answering (almost) natural questions
Xinya Du and Claire Cardie. 2020 · 2020
Later among the works it cites.
Event extraction as machine reading comprehension
Jian Liu, Yubo Chen, Kang Liu, Wei Bi, and Xiaojiang Liu. 2020 · 2020
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Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.