  Dialog state tracking is used to estimate the current belief state of a
dialog given all the preceding conversation. Machine reading comprehension, on
the other hand, focuses on building systems that read passages of text and
answer questions that require some understanding of passages. We formulate
dialog state tracking as a reading comprehension task to answer the question
$what\ is\ the\ state\ of\ the\ current\ dialog?$ after reading conversational
context. In contrast to traditional state tracking methods where the dialog
state is often predicted as a distribution over a closed set of all the
possible slot values within an ontology, our method uses a simple
attention-based neural network to point to the slot values within the
conversation. Experiments on MultiWOZ-2.0 cross-domain dialog dataset show that
our simple system can obtain similar accuracies compared to the previous more
complex methods. By exploiting recent advances in contextual word embeddings,
adding a model that explicitly tracks whether a slot value should be carried
over to the next turn, and combining our method with a traditional joint state
tracking method that relies on closed set vocabulary, we can obtain a
joint-goal accuracy of $47.33\%$ on the standard test split, exceeding current
state-of-the-art by $11.75\%$**.
