Hence, obtaining expert manual annotations in complex domains is infeasible as it is tedious to expert-annotate answers that may be found across long document collections (e.g., longitudinal EMR) (Lee et al., 2017).
we reverse engineer the process where we reuse expert annotations available in NLP tasks such as entity recognition, coreference, and relation learning, based on the information captured in the logical forms to populate entity placeholders in templates and generate answers.