Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words
Haochun Wang, Chi Liu, Nuwa Xi, Sendong Zhao, Meizhi Ju, Shiwei Zhang, Ziheng Zhang, Yefeng Zheng, Bing Qin, Ting Liu
Correct Metadata for
Abstract
Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain. However, tuning with prompt in biomedical domain has not been investigated thoroughly. Biomedical words are often rare in general domain, but quite ubiquitous in biomedical contexts, which dramatically deteriorates the performance of pre-trained models on downstream biomedical applications even after fine-tuning, especially in low-resource scenarios. We propose a simple yet effective approach to helping models learn rare biomedical words during tuning with prompt. Experimental results show that our method can achieve up to 6% improvement in biomedical natural language inference task without any extra parameters or training steps using few-shot vanilla prompt settings.- Anthology ID:
- 2022.coling-1.122
- 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:
- 1422–1431
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.122/
- DOI:
- Bibkey:
- Cite (ACL):
- Haochun Wang, Chi Liu, Nuwa Xi, Sendong Zhao, Meizhi Ju, Shiwei Zhang, Ziheng Zhang, Yefeng Zheng, Bing Qin, and Ting Liu. 2022. Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1422–1431, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words (Wang et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.122.pdf
- Code
- s65b40/prompt_n_paraphrase
- Data
- MIMIC-III
Export citation
@inproceedings{wang-etal-2022-prompt,
title = "Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words",
author = "Wang, Haochun and
Liu, Chi and
Xi, Nuwa and
Zhao, Sendong and
Ju, Meizhi and
Zhang, Shiwei and
Zhang, Ziheng and
Zheng, Yefeng and
Qin, Bing and
Liu, Ting",
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-1.122/",
pages = "1422--1431",
abstract = "Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain. However, tuning with prompt in biomedical domain has not been investigated thoroughly. Biomedical words are often rare in general domain, but quite ubiquitous in biomedical contexts, which dramatically deteriorates the performance of pre-trained models on downstream biomedical applications even after fine-tuning, especially in low-resource scenarios. We propose a simple yet effective approach to helping models learn rare biomedical words during tuning with prompt. Experimental results show that our method can achieve up to 6{\%} improvement in biomedical natural language inference task without any extra parameters or training steps using few-shot vanilla prompt settings."
}
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%0 Conference Proceedings %T Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words %A Wang, Haochun %A Liu, Chi %A Xi, Nuwa %A Zhao, Sendong %A Ju, Meizhi %A Zhang, Shiwei %A Zhang, Ziheng %A Zheng, Yefeng %A Qin, Bing %A Liu, Ting %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 wang-etal-2022-prompt %X Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain. However, tuning with prompt in biomedical domain has not been investigated thoroughly. Biomedical words are often rare in general domain, but quite ubiquitous in biomedical contexts, which dramatically deteriorates the performance of pre-trained models on downstream biomedical applications even after fine-tuning, especially in low-resource scenarios. We propose a simple yet effective approach to helping models learn rare biomedical words during tuning with prompt. Experimental results show that our method can achieve up to 6% improvement in biomedical natural language inference task without any extra parameters or training steps using few-shot vanilla prompt settings. %U https://aclanthology.org/2022.coling-1.122/ %P 1422-1431
Markdown (Informal)
[Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words](https://aclanthology.org/2022.coling-1.122/) (Wang et al., COLING 2022)
- Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words (Wang et al., COLING 2022)
ACL
- Haochun Wang, Chi Liu, Nuwa Xi, Sendong Zhao, Meizhi Ju, Shiwei Zhang, Ziheng Zhang, Yefeng Zheng, Bing Qin, and Ting Liu. 2022. Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1422–1431, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.