  Goal-oriented dialog systems, which can be trained end-to-end without
manually encoding domain-specific features, show tremendous promise in the
customer support use-case e.g. flight booking, hotel reservation, technical
support, student advising etc. These dialog systems must learn to interact with
external domain knowledge to achieve the desired goal e.g. recommending courses
to a student, booking a table at a restaurant etc. This paper presents extended
Enhanced Sequential Inference Model (ESIM) models: a) K-ESIM (Knowledge-ESIM),
which incorporates the external domain knowledge and b) T-ESIM (Targeted-ESIM),
which leverages information from similar conversations to improve the
prediction accuracy. Our proposed models and the baseline ESIM model are
evaluated on the Ubuntu and Advising datasets in the Sentence Selection track
of the latest Dialog System Technology Challenge (DSTC7), where the goal is to
find the correct next utterance, given a partial conversation, from a set of
candidates. Our preliminary results suggest that incorporating external
knowledge sources and leveraging information from similar dialogs leads to
performance improvements for predicting the next utterance.
