  When evaluating an answer choice for Reading Comprehension task, other answer
choices available for the question and the answers of related questions about
the same paragraph often provide valuable information. In this paper, we
propose a method to leverage the natural language relations between the answer
choices, such as entailment and contradiction, to improve the performance of
machine comprehension. We use a stand-alone question answering (QA) system to
perform QA task and a Natural Language Inference (NLI) system to identify the
relations between the choice pairs. Then we perform inference using an Integer
Linear Programming (ILP)-based relational framework to re-evaluate the
decisions made by the standalone QA system in light of the relations identified
by the NLI system. We also propose a multitask learning model that learns both
the tasks jointly.
