  Open book question answering is a type of natural language based QA (NLQA)
where questions are expected to be answered with respect to a given set of open
book facts, and common knowledge about a topic. Recently a challenge involving
such QA, OpenBookQA, has been proposed. Unlike most other NLQA tasks that focus
on linguistic understanding, OpenBookQA requires deeper reasoning involving
linguistic understanding as well as reasoning with common knowledge. In this
paper we address QA with respect to the OpenBookQA dataset and combine state of
the art language models with abductive information retrieval (IR), information
gain based re-ranking, passage selection and weighted scoring to achieve 72.0%
accuracy, an 11.6% improvement over the current state of the art.
