Discovering New Intents with Deep Aligned Clustering
This work provides an incremental improvement for dialogue system developers by offering a more robust method for discovering new intents, particularly when limited known intent data is available.
This paper addresses the problem of discovering new intents in dialogue systems, which is challenging due to the difficulty in transferring prior knowledge and generating high-quality supervised signals. The proposed Deep Aligned Clustering method leverages limited known intent data to pre-train a model, uses k-means for pseudo-label generation, and introduces an alignment strategy to handle label inconsistency, resulting in substantial improvements over state-of-the-art methods on two benchmark datasets.
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.