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Found 5 papers, 4 papers with code
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.
Asymmetric feature interaction for interpreting model predictions
In this work, we propose an asymmetric feature interaction attribution explanation model that aims to explore asymmetric higher-order feature interactions in the inference of deep neural NLP models.
New Intent Discovery with Pre-training and Contrastive Learning
Existing approaches typically rely on a large amount of labeled utterances and employ pseudo-labeling methods for representation learning and clustering, which are label-intensive, inefficient, and inaccurate.
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.
Effectiveness of Pre-training for Few-shot Intent Classification
This paper investigates the effectiveness of pre-training for few-shot intent classification.
