Curriculum Contrastive Learning for COVID-19 FAQ Retrieval
2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
; : 3228-3234, 2022.
Article
in English
| Scopus | ID: covidwho-2223083
ABSTRACT
Medical Frequently Asked Question (FAQ) retrieval aims to find the most relevant question-answer pairs for a given user query, which is of great significance for enhancing people medical health awareness and knowledge. However, due to medical data privacy and labor-intensive labeling, there is a lack of large-scale question-matching training datasets. Previous methods directly use the collected question-answer pairs on search engines to train retrieval models, which achieved poor performance. Inspired by recent advances in contrastive learning, we propose a novel contrastive curriculum learning framework for modeling user medical queries. First, we design different data augmentation methods to generate positive samples and different types of negative samples. Second, we propose a curriculum learning strategy that associates difficulty levels with negative samples. Through a contrastive learning process from easy to hard, our method achieves excellent results on two medical datasets. © 2022 IEEE.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS