Continual Few-shot Intent Detection (2022)

Abstract: Intent detection is at the core of task-oriented dialogue systems. Existing intent detection systems are typically trained with a large amount of data over a predefined set of intent classes. However, newly emerged intents in multiple domains are commonplace in the real world. And it is time-consuming and impractical for dialogue systems to re-collect enough annotated data and re-train the model. These limitations call for an intent detection system that could continually recognize new intents with very few labeled examples. In this work, we study the Continual Few-shot Intent Detection (CFID) problem and construct a benchmark consisting of nine tasks with multiple domains and imbalanced classes. To address the key challenges of (a) catastrophic forgetting during continuous learning and (b) negative knowledge transfer across tasks, we propose the Prefix-guided Lightweight Encoder (PLE) with three auxiliary strategies, namely Pseudo Samples Replay (PSR), Teacher Knowledge Transfer (TKT) and Dynamic Weighting Replay (DWR). Extensive experiments demonstrate the effectiveness and efficiency of our method in preventing catastrophic forgetting and encouraging positive knowledge transfer across tasks.

## introduction intent detection (id) is at the core of task-oriented dialogue systems. it aims at understanding the goals underlying user utterances and classifying them into different intents accurately @xcite . traditionally, the id system is trained with plenty of labeled data to identify a predefined set of intent classes @xcite . however, newly emerged intents in multiple domains are commonplace in the real scenario. a naive approach to detecting new intents is to recollect annotated data and re-train the model, which ## related work traditional intent detection aims to classify intent in the utterance, which can be defined as a sentence classification task. popular approaches such as @xcite ; @xcite ; @xcite have achieved promising performance. however, such methods heavily rely on large amounts of labeled data. few-shot intent detection aims to classify accurately identify intents in few-shot settings. @xcite solves it as a textual entailment problem and uses large-scale entailment datasets for pre-training. however, it is time-consuming and expensive to train with hundreds of intents. @xcite proposes an example-driven strategy to tackle this task, which learns to classify utterances by comparing them to examples. @xcite and @xcite solve the data scarcity by leveraging the label names or augmented samples. more recently, @xcite show the effectiveness of pre-training and contrastive fine-tuning on this task. continual learning aims to learn a sequence of tasks incrementally. most works in nlp domains focus on text classification tasks in continual settings @xcite @xcite . the main problem for continual text classification is catastrophic forgetting and replay-based methods @xcite have been proven promising to alleviate the problem, which retain a few examples in previous tasks and continually replay them with new tasks. continual intent detection. recently, @xcite and @xcite have made some efforts on the continual intent detection task (cid). however, they did not further investigate with the few-shot setting, which is more challenging and crucial for the low-resource dialogue systems. the most similar to our work is @xcite , which firstly proposes the few-shot class-incremental learning for intent detection (fscil-id). however, it is not aligned with the real scenario for the following reasons: (i) all tasks belong to the same domain without considering the domain gaps of different intents. (ii) the number of classes in emerging new tasks is fixed without considering the imbalance of classes across tasks in real systems. ## conclusion in this paper, we define a more challenging yet practical problem as continual few-shot intent detection (cfid), where the system needs to handle continually emerging new intents with very few labeled data. to deal with the problem, we propose a novel prefix-guided lightweight encoder with three auxiliary strategies. extensive experiments demonstrate the effectiveness and efficiency of our method in preventing catastrophic forgetting and encouraging positive knowledge transfer across tasks.