  In multi-turn dialog, utterances do not always take the full form of
sentences \cite{Carbonell1983DiscoursePA}, which naturally makes understanding
the dialog context more difficult. However, it is essential to fully grasp the
dialog context to generate a reasonable response. Hence, in this paper, we
propose to improve the response generation performance by examining the model's
ability to answer a reading comprehension question, where the question is
focused on the omitted information in the dialog. Enlightened by the multi-task
learning scheme, we propose a joint framework that unifies these two tasks,
sharing the same encoder to extract the common and task-invariant features with
different decoders to learn task-specific features. To better fusing
information from the question and the dialog history in the encoding part, we
propose to augment the Transformer architecture with a memory updater, which is
designed to selectively store and update the history dialog information so as
to support downstream tasks. For the experiment, we employ human annotators to
write and examine a large-scale dialog reading comprehension dataset. Extensive
experiments are conducted on this dataset, and the results show that the
proposed model brings substantial improvements over several strong baselines on
both tasks. In this way, we demonstrate that reasoning can indeed help better
response generation and vice versa. We release our large-scale dataset for
further research.
