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Found 4 papers, 3 papers with code
A Closer Look at Few-Shot Out-of-Distribution Intent Detection
Comprehensive experiments on three real-world intent detection benchmark datasets demonstrate the high effectiveness of our proposed approach and its great potential in improving state-of-the-art methods for few-shot OOD intent detection.
Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training
We consider the task of few-shot intent detection, which involves training a deep learning model to classify utterances based on their underlying intents using only a small amount of labeled data.
Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization
It is challenging to train a good intent classifier for a task-oriented dialogue system with only a few annotations.
Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training
Since the distribution of outlier utterances is arbitrary and unknown in the training stage, existing methods commonly rely on strong assumptions on data distribution such as mixture of Gaussians to make inference, resulting in either complex multi-step training procedures or hand-crafted rules such as confidence threshold selection for outlier detection.
