  Discovering new intents is a crucial task in dialogue systems. Most existing
methods are limited in transferring the prior knowledge from known intents to
new intents. They also have difficulties in providing high-quality supervised
signals to learn clustering-friendly features for grouping unlabeled intents.
In this work, we propose an effective method, Deep Aligned Clustering, to
discover new intents with the aid of the limited known intent data. Firstly, we
leverage a few labeled known intent samples as prior knowledge to pre-train the
model. Then, we perform k-means to produce cluster assignments as
pseudo-labels. Moreover, we propose an alignment strategy to tackle the label
inconsistency problem during clustering assignments. Finally, we learn the
intent representations under the supervision of the aligned pseudo-labels. With
an unknown number of new intents, we predict the number of intent categories by
eliminating low-confidence intent-wise clusters. Extensive experiments on two
benchmark datasets show that our method is more robust and achieves substantial
improvements over the state-of-the-art methods. The codes are released at
https://github.com/thuiar/DeepAligned-Clustering.
