Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading Comprehension

Tongtao Ling, Lei Chen, Huangxu Sheng, Zicheng Cai, Hai-Lin Liu
6/25/2023
cs.CL

No official code found. Best implementation: rickltt/event_detection (11 stars, last commit 2024-02-03).

Abstract

The traditional way of sentence-level event detection involves two important subtasks: trigger identification and trigger classifications, where the identified event trigger words are used to classify event types from sentences. However, trigger classification highly depends on abundant annotated trigger words and the accuracy of trigger identification. In a real scenario, annotating trigger words is time-consuming and laborious. For this reason, we propose a trigger-free event detection model, which transforms event detection into a two-tower model based on machine reading comprehension and prompt learning. Compared to existing trigger-based and trigger-free methods, experimental studies on two event detection benchmark datasets (ACE2005 and MAVEN) have shown that the proposed approach can achieve competitive performance.

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Code Implementations(1)

rickltt/event_detectionCommunity100%

Source code of EDPRC.

111Shell, PythonNov 4, 20222 years ago

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Cite this paper

@article{ling2023sentencelevel,
  title  = {Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading Comprehension},
  author = {Tongtao Ling and Lei Chen and Huangxu Sheng and Zicheng Cai and Hai-Lin Liu},
  year   = {2023},
  eprint = {2306.14176},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.14176v1}
}

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