  Conventional Intent Detection (ID) models are usually trained offline, which
relies on a fixed dataset and a predefined set of intent classes. However, in
real-world applications, online systems usually involve continually emerging
new user intents, which pose a great challenge to the offline training
paradigm. Recently, lifelong learning has received increasing attention and is
considered to be the most promising solution to this challenge. In this paper,
we propose Lifelong Intent Detection (LID), which continually trains an ID
model on new data to learn newly emerging intents while avoiding
catastrophically forgetting old data. Nevertheless, we find that existing
lifelong learning methods usually suffer from a serious imbalance between old
and new data in the LID task. Therefore, we propose a novel lifelong learning
method, Multi-Strategy Rebalancing (MSR), which consists of cosine
normalization, hierarchical knowledge distillation, and inter-class margin loss
to alleviate the multiple negative effects of the imbalance problem.
Experimental results demonstrate the effectiveness of our method, which
significantly outperforms previous state-of-the-art lifelong learning methods
on the ATIS, SNIPS, HWU64, and CLINC150 benchmarks.
