  Most dialogue systems in real world rely on predefined intents and answers
for QA service, so discovering potential intents from large corpus previously
is really important for building such dialogue services. Considering that most
scenarios have few intents known already and most intents waiting to be
discovered, we focus on semi-supervised text clustering and try to make the
proposed method benefit from labeled samples for better overall clustering
performance. In this paper, we propose Deep Contrastive Semi-supervised
Clustering (DCSC), which aims to cluster text samples in a semi-supervised way
and provide grouped intents to operation staff. To make DCSC fully utilize the
limited known intents, we propose a two-stage training procedure for DCSC, in
which DCSC will be trained on both labeled samples and unlabeled samples, and
achieve better text representation and clustering performance. We conduct
experiments on two public datasets to compare our model with several popular
methods, and the results show DCSC achieve best performance across all datasets
and circumstances, indicating the effect of the improvements in our work.
