Effectiveness of Pre-training for Few-shot Intent Classification

• URL: http://arxiv.org/abs/2109.05782v2
• Date: Sun, 15 Sep 2024 16:28:00 GMT
• Title: Effectiveness of Pre-training for Few-shot Intent Classification
• Authors: Haode Zhang, Yuwei Zhang, Li-Ming Zhan, Jiaxin Chen, Guangyuan Shi, Albert Y. S. Lam, Xiao-Ming Wu,
• Abstract summary: This paper investigates the effectiveness of pre-training for few-shot intent classification. We find it highly effective and efficient to simply fine-tune BERT with a small set of labeled utterances from public datasets. IntentBERT can easily surpass the performance of existing pre-trained models for few-shot intent classification on novel domains.
• Score: 33.557100231606505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper investigates the effectiveness of pre-training for few-shot intent classification. While existing paradigms commonly further pre-train language models such as BERT on a vast amount of unlabeled corpus, we find it highly effective and efficient to simply fine-tune BERT with a small set of labeled utterances from public datasets. Specifically, fine-tuning BERT with roughly 1,000 labeled data yields a pre-trained model -- IntentBERT, which can easily surpass the performance of existing pre-trained models for few-shot intent classification on novel domains with very different semantics. The high effectiveness of IntentBERT confirms the feasibility and practicality of few-shot intent detection, and its high generalization ability across different domains suggests that intent classification tasks may share a similar underlying structure, which can be efficiently learned from a small set of labeled data. The source code can be found at https://github.com/hdzhang-code/IntentBERT.

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