We propose a novel methodology to generate domain-specific large-scale question answering (QA) datasets by re-purposing existing annotations for other NLP tasks. We demonstrate an instance of this methodology in generating a large-scale QA dataset for electronic medical records by leveraging existing expert annotations on clinical notes for various NLP tasks from the community shared i2b2 datasets § . The resulting corpus (emrQA) has 1 million question-logical form and 400,000+ questionanswer evidence pairs. We characterize the dataset and explore its learning potential by training baseline models for question to logical form and question to answer mapping.
Several large-scale automatically collected biomedical QA datasets have been introduced: emrQA REF ) is an extractive QA dataset for electronic medical records (EHR) built by re-purposing existing annotations on EHR corpora.
emrQA: A Large Corpus for Question Answering on Electronic Medical Records
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Computer Science