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Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection
AbstractFew-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen …...
Few-shot Pseudo-Labeling for Intent Detection
AbstractIn this paper, we introduce a state-of-the-art pseudo-labeling technique for few-shot intent detection. We devise a folding/unfolding hierarchical clustering algorithm which assigns weighted pseudo-labels to unlabeled user utterances. We show …...
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning
AbstractIn this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar. We present a simple yet effective few-shot intent detection …...
PROTAUGMENT: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning
AbstractRecent research considers few-shot intent detection as a meta-learning problem: the model is learning to learn from a consecutive set of small tasks named episodes. In this work, we propose …...
Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization
AbstractIt is challenging to train a good intent classifier for a task-oriented dialogue system with only a few annotations. Recent studies have shown that fine-tuning pre-trained language models with a …...
Dual Class Knowledge Propagation Network for Multi-label Few-shot Intent Detection
AbstractMulti-label intent detection aims to assign multiple labels to utterances and attracts increasing attention as a practical task in task-oriented dialogue systems. As dialogue domains change rapidly and new intents …...