  Machine based text comprehension has always been a significant research field
in natural language processing. Once a full understanding of the text context
and semantics is achieved, a deep learning model can be trained to solve a
large subset of tasks, e.g. text summarization, classification and question
answering. In this paper we focus on the question answering problem,
specifically the multiple choice type of questions. We develop a model based on
BERT, a state-of-the-art transformer network. Moreover, we alleviate the
ability of BERT to support large text corpus by extracting the highest
influence sentences through a semantic similarity model. Evaluations of our
proposed model demonstrate that it outperforms the leading models in the
MovieQA challenge and we are currently ranked first in the leader board with
test accuracy of 87.79%. Finally, we discuss the model shortcomings and suggest
possible improvements to overcome these limitations.
