  New intent discovery aims to uncover novel intent categories from user
utterances to expand the set of supported intent classes. It is a critical task
for the development and service expansion of a practical dialogue system.
Despite its importance, this problem remains under-explored in the literature.
Existing approaches typically rely on a large amount of labeled utterances and
employ pseudo-labeling methods for representation learning and clustering,
which are label-intensive, inefficient, and inaccurate. In this paper, we
provide new solutions to two important research questions for new intent
discovery: (1) how to learn semantic utterance representations and (2) how to
better cluster utterances. Particularly, we first propose a multi-task
pre-training strategy to leverage rich unlabeled data along with external
labeled data for representation learning. Then, we design a new contrastive
loss to exploit self-supervisory signals in unlabeled data for clustering.
Extensive experiments on three intent recognition benchmarks demonstrate the
high effectiveness of our proposed method, which outperforms state-of-the-art
methods by a large margin in both unsupervised and semi-supervised scenarios.
The source code will be available at
\url{https://github.com/zhang-yu-wei/MTP-CLNN}.
