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BioADAPT-MRC: adversarial learning-based domain adaptation improves biomedical machine reading comprehension task.

Maria Mahbub^1,2, Sudarshan Srinivasan², Edmon Begoli²

¹Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA.

Bioinformatics (Oxford, England)

|July 25, 2022

Summary

This summary is machine-generated.

This study introduces BioADAPT-MRC, an adversarial learning framework for biomedical machine reading comprehension. BioADAPT-MRC achieves state-of-the-art results without requiring biomedical-specific labeled data.

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Area of Science:

Biomedical informatics
Natural Language Processing
Machine Learning

Background:

Biomedical machine reading comprehension (biomedical-MRC) systems require large, human-annotated datasets for high performance.
Domain knowledge requirement for biomedical data leads to scarcity of labeled data.
Direct transfer learning from general domains to biomedical domains is hindered by distribution discrepancies.

Purpose of the Study:

To develop a domain adaptation framework for biomedical-MRC.
To address the marginal distribution discrepancies between general and biomedical datasets.
To improve the performance of biomedical-MRC models without relying on biomedical-specific labeled data.

Main Methods:

An adversarial learning-based domain adaptation framework, BioADAPT-MRC, was developed.
The framework utilizes neural networks to bridge the distribution gap between domains.
It relaxes the need for pseudo-label generation for training.

Main Results:

BioADAPT-MRC achieved state-of-the-art performance on benchmark biomedical-MRC datasets (BioASQ-7b, 8b, 9b).
The framework demonstrated effectiveness without using any synthetic or human-annotated biomedical data.
Performance was evaluated against existing state-of-the-art methods.

Conclusions:

BioADAPT-MRC effectively adapts general domain models to the biomedical domain for MRC tasks.
The framework offers a viable solution for overcoming data scarcity in biomedical NLP.
Open-source availability facilitates further research and application.