KiPT: Knowledge-injected Prompt Tuning for Event Detection

Haochen Li, Tong Mo, Hongcheng Fan, Jingkun Wang, Jiaxi Wang, Fuhao Zhang, Weiping Li

Abstract
Event detection aims to detect events from the text by identifying and classifying event triggers (the most representative words). Most of the existing works rely heavily on complex downstream networks and require sufficient training data. Thus, those models may be structurally redundant and perform poorly when data is scarce. Prompt-based models are easy to build and are promising for few-shot tasks. However, current prompt-based methods may suffer from low precision because they have not introduced event-related semantic knowledge (e.g., part of speech, semantic correlation, etc.). To address these problems, this paper proposes a Knowledge-injected Prompt Tuning (KiPT) model. Specifically, the event detection task is formulated into a condition generation task. Then, knowledge-injected prompts are constructed using external knowledge bases, and a prompt tuning strategy is leveraged to optimize the prompts. Extensive experiments indicate that KiPT outperforms strong baselines, especially in few-shot scenarios.
Anthology ID:
2022.coling-1.169
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1943–1952
Language:
URL:
https://aclanthology.org/2022.coling-1.169
DOI:
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Cite (ACL):
Haochen Li, Tong Mo, Hongcheng Fan, Jingkun Wang, Jiaxi Wang, Fuhao Zhang, and Weiping Li. 2022. KiPT: Knowledge-injected Prompt Tuning for Event Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1943–1952, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
KiPT: Knowledge-injected Prompt Tuning for Event Detection (Li et al., COLING 2022)
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https://preview.aclanthology.org/ingestion-script-update/2022.coling-1.169.pdf
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