  While in recent years machine learning (ML) based approaches have been the
popular approach in developing end-to-end question answering systems, such
systems often struggle when additional knowledge is needed to correctly answer
the questions. Proposed alternatives involve translating the question and the
natural language text to a logical representation and then use logical
reasoning. However, this alternative falters when the size of the text gets
bigger. To address this we propose an approach that does logical reasoning over
premises written in natural language text. The proposed method uses recent
features of Answer Set Programming (ASP) to call external NLP modules (which
may be based on ML) which perform simple textual entailment. To test our
approach we develop a corpus based on the life cycle questions and showed that
Our system achieves up to $18\%$ performance gain when compared to standard MCQ
solvers.
