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The onset of the COVID-19 pandemic accentuated the need for access tobiomedical literature to answer timely and disease-specific questions. Duringthe early days of the pandemic, one of the biggest challenges we faced was thelack of peer-reviewed biomedical articles on COVID-19 that could be used totrain machine learning models for question answering (QA). In this paper, weexplore the roles weak supervision and data augmentation play in training deepneural network QA models. First, we investigate whether labels generatedautomatically from the structured abstracts of scholarly papers using aninformation retrieval algorithm, BM25, provide a weak supervision signal totrain an extractive QA model. We also curate new QA pairs using informationretrieval techniques, guided by the clinicaltrials.gov schema and thestructured abstracts of articles, in the absence of annotated data frombiomedical domain experts. Furthermore, we explore augmenting the training dataof a deep neural network model with linguistic features from external sourcessuch as lexical databases to account for variations in word morphology andmeaning. To better utilize our training data, we apply curriculum learning todomain adaptation, fine-tuning our QA model in stages based on characteristicsof the QA pairs. We evaluate our methods in the context of QA models at thecore of a system to answer questions about COVID-19.

Using Weak Supervision and Data Augmentation in Question Answering