Repository for COLING 2022 paper "Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words"
We conducted our experiments on two biomedical datasets MedNLI and MedSTS. Both datasets are ONLY accessible with the corresponding confidentiality agreement signed.
https://physionet.org/content/mednli/1.0.0/
https://arxiv.org/abs/1808.09397
The MedSTS can be accessed by E-mail contact with the data maintainers.
As introduced in the paper, the rare biomedical words are retrieved through an API provided by an online dictionary - "Wiktionary".
We consider the words with tags including 'medical', 'medicine', 'disease', 'symptom', and 'pharma-' as biomedical words. We loop through the common pre-training corpora for biomedical pre-trained models to obtain the word frequency for all possible words. Rare words are the words whose frequency in the pre-training corpora is smaller than a manually-selected threshold. Here, we provide the rare biomedical word list with a threshold of 200k and the corresponding paraphrases in the file paraphrase.txt.
If you have any question about our work, feel free to open an issue or email me ([email protected]).
@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",
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",
}