Abstract
This paper studies event causality identification, which aims at predicting the causality relation for a pair of events in a sentence. Regarding event causality identification as a supervised classification task, most existing methods suffer from the problem of insufficient annotated data. In this paper, we propose a new derivative prompt joint learning model for event causality identification, which leverages potential causal knowledge in the pre-trained language model to tackle the data scarcity problem. Specifically, rather than external data or knowledge augmentation, we derive two relevant prompt tasks from event causality identification to enhance the model’s ability to identify explicit and implicit causality. We evaluate our model on two benchmark datasets and the results show that our model has great advantages over previous methods.- Anthology ID:
- 2022.coling-1.200
- 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:
- 2288–2299
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.200
- DOI:
- Bibkey:
- Cite (ACL):
- Shirong Shen, Heng Zhou, Tongtong Wu, and Guilin Qi. 2022. Event Causality Identification via Derivative Prompt Joint Learning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2288–2299, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Event Causality Identification via Derivative Prompt Joint Learning (Shen et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.200.pdf
- Code
- Everglow123/ECIMP
Export citation
@inproceedings{shen-etal-2022-event,
title = "Event Causality Identification via Derivative Prompt Joint Learning",
author = "Shen, Shirong and
Zhou, Heng and
Wu, Tongtong and
Qi, Guilin",
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-1.200",
pages = "2288--2299",
abstract = "This paper studies event causality identification, which aims at predicting the causality relation for a pair of events in a sentence. Regarding event causality identification as a supervised classification task, most existing methods suffer from the problem of insufficient annotated data. In this paper, we propose a new derivative prompt joint learning model for event causality identification, which leverages potential causal knowledge in the pre-trained language model to tackle the data scarcity problem. Specifically, rather than external data or knowledge augmentation, we derive two relevant prompt tasks from event causality identification to enhance the model{'}s ability to identify explicit and implicit causality. We evaluate our model on two benchmark datasets and the results show that our model has great advantages over previous methods.",
}
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%0 Conference Proceedings %T Event Causality Identification via Derivative Prompt Joint Learning %A Shen, Shirong %A Zhou, Heng %A Wu, Tongtong %A Qi, Guilin %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 shen-etal-2022-event %X This paper studies event causality identification, which aims at predicting the causality relation for a pair of events in a sentence. Regarding event causality identification as a supervised classification task, most existing methods suffer from the problem of insufficient annotated data. In this paper, we propose a new derivative prompt joint learning model for event causality identification, which leverages potential causal knowledge in the pre-trained language model to tackle the data scarcity problem. Specifically, rather than external data or knowledge augmentation, we derive two relevant prompt tasks from event causality identification to enhance the model’s ability to identify explicit and implicit causality. We evaluate our model on two benchmark datasets and the results show that our model has great advantages over previous methods. %U https://aclanthology.org/2022.coling-1.200 %P 2288-2299
Markdown (Informal)
[Event Causality Identification via Derivative Prompt Joint Learning](https://aclanthology.org/2022.coling-1.200) (Shen et al., COLING 2022)
- Event Causality Identification via Derivative Prompt Joint Learning (Shen et al., COLING 2022)
ACL
- Shirong Shen, Heng Zhou, Tongtong Wu, and Guilin Qi. 2022. Event Causality Identification via Derivative Prompt Joint Learning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2288–2299, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.