  Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend
complex biomedical narratives and assist healthcare professionals in retrieving
information from them. The high performance of modern neural network-based MRC
systems depends on high-quality, large-scale, human-annotated training
datasets. In the biomedical domain, a crucial challenge in creating such
datasets is the requirement for domain knowledge, inducing the scarcity of
labeled data and the need for transfer learning from the labeled
general-purpose (source) domain to the biomedical (target) domain. However,
there is a discrepancy in marginal distributions between the general-purpose
and biomedical domains due to the variances in topics. Therefore,
direct-transferring of learned representations from a model trained on a
general-purpose domain to the biomedical domain can hurt the model's
performance. We present an adversarial learning-based domain adaptation
framework for the biomedical machine reading comprehension task (BioADAPT-MRC),
a neural network-based method to address the discrepancies in the marginal
distributions between the general and biomedical domain datasets. BioADAPT-MRC
relaxes the need for generating pseudo labels for training a well-performing
biomedical-MRC model. We extensively evaluate the performance of BioADAPT-MRC
by comparing it with the best existing methods on three widely used benchmark
biomedical-MRC datasets -- BioASQ-7b, BioASQ-8b, and BioASQ-9b. Our results
suggest that without using any synthetic or human-annotated data from the
biomedical domain, BioADAPT-MRC can achieve state-of-the-art performance on
these datasets. Availability: BioADAPT-MRC is freely available as an
open-source project at \url{https://github.com/mmahbub/BioADAPT-MRC}.
