  There are several dialog frameworks which allow manual specification of
intents and rule based dialog flow. The rule based framework provides good
control to dialog designers at the expense of being more time consuming and
laborious. The job of a dialog designer can be reduced if we could identify
pairs of user intents and corresponding responses automatically from prior
conversations between users and agents. In this paper we propose an approach to
find these frequent user utterances (which serve as examples for intents) and
corresponding agent responses. We propose a novel SimCluster algorithm that
extends standard K-means algorithm to simultaneously cluster user utterances
and agent utterances by taking their adjacency information into account. The
method also aligns these clusters to provide pairs of intents and response
groups. We compare our results with those produced by using simple Kmeans
clustering on a real dataset and observe upto 10% absolute improvement in
F1-scores. Through our experiments on synthetic dataset, we show that our
algorithm gains more advantage over K-means algorithm when the data has large
variance.
