  As one promising way to inquire about any particular information through a
dialog with the bot, question answering dialog systems have gained increasing
research interests recently. Designing interactive QA systems has always been a
challenging task in natural language processing and used as a benchmark to
evaluate a machine's ability of natural language understanding. However, such
systems often struggle when the question answering is carried out in multiple
turns by the users to seek more information based on what they have already
learned, thus, giving rise to another complicated form called Conversational
Question Answering (CQA). CQA systems are often criticized for not
understanding or utilizing the previous context of the conversation when
answering the questions. To address the research gap, in this paper, we explore
how to integrate conversational history into the neural machine comprehension
system. On one hand, we introduce a framework based on a publically available
pre-trained language model called BERT for incorporating history turns into the
system. On the other hand, we propose a history selection mechanism that
selects the turns that are relevant and contributes the most to answer the
current question. Experimentation results revealed that our framework is
comparable in performance with the state-of-the-art models on the QuAC leader
board. We also conduct a number of experiments to show the side effects of
using entire context information which brings unnecessary information and noise
signals resulting in a decline in the model's performance.
