Abstract
Sentence-level event detection has traditionally been carried out in two key steps: trigger identification and trigger classification. The trigger words first are identified from sentences and then utilized to categorize event types. However, this classification hugely relies on a substantial amount of annotated trigger words along with the accuracy of the trigger identification process. This annotation of trigger words is labor-intensive and time-consuming in real-world environments. As a solution to this, we propose a model that does not require any triggers for event detection. This model reformulates event detection into a two-tower model that uses machine learning comprehension and prompt learning. Compared to the existing methods, which are either trigger-based or trigger-free, experimental studies on two benchmark event detection datasets (ACE2005 and MAVEN) reveal that our proposed method can achieve competitive performance.
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Notes
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For example, we convert event token employment to “\(\langle employment \rangle \)” and add it to vocabulary. All events operate like this. In addition, we add a special token “\(\langle none \rangle \)” that no events have occurred.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (62006044, 62172110). Additionally, support was also partly provided by the Natural Science Foundation of Guangdong Province (2022A1515010130), and the Programme of Science and Technology of Guangdong Province (2021A0505110004) contributed in part to this work.
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Ling, T., Chen, L., Sheng, H., Cai, Z., Liu, HL. (2023). Sentence-Level Event Detection Without Triggers via Prompt Learning and Machine Reading Comprehension. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_3
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