KiPT: Knowledge-injected Prompt Tuning for Event Detection
Haochen Li, Tong Mo, Hongcheng Fan, Jingkun Wang, Jiaxi Wang, Fuhao Zhang, Weiping Li
Correct Metadata for
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
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1943–1952
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.169/
- DOI:
- Bibkey:
- 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)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.169.pdf
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@inproceedings{li-etal-2022-kipt,
title = "{K}i{PT}: Knowledge-injected Prompt Tuning for Event Detection",
author = "Li, Haochen and
Mo, Tong and
Fan, Hongcheng and
Wang, Jingkun and
Wang, Jiaxi and
Zhang, Fuhao and
Li, Weiping",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.169/",
pages = "1943--1952",
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."
}
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<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.</abstract>
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%0 Conference Proceedings %T KiPT: Knowledge-injected Prompt Tuning for Event Detection %A Li, Haochen %A Mo, Tong %A Fan, Hongcheng %A Wang, Jingkun %A Wang, Jiaxi %A Zhang, Fuhao %A Li, Weiping %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F li-etal-2022-kipt %X 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. %U https://aclanthology.org/2022.coling-1.169/ %P 1943-1952
Markdown (Informal)
[KiPT: Knowledge-injected Prompt Tuning for Event Detection](https://aclanthology.org/2022.coling-1.169/) (Li et al., COLING 2022)
- KiPT: Knowledge-injected Prompt Tuning for Event Detection (Li et al., COLING 2022)
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.