@inproceedings{zhang-etal-2021-effectiveness-pre,
title = "Effectiveness of Pre-training for Few-shot Intent Classification",
author = "Zhang, Haode and
Zhang, Yuwei and
Zhan, Li-Ming and
Chen, Jiaxin and
Shi, Guangyuan and
Wu, Xiao-Ming and
Lam, Albert Y.S.",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.96",
doi = "10.18653/v1/2021.findings-emnlp.96",
pages = "1114--1120",
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 \url{https://github.com/hdzhang-code/IntentBERT}.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Effectiveness of Pre-training for Few-shot Intent Classification
%A Zhang, Haode
%A Zhang, Yuwei
%A Zhan, Li-Ming
%A Chen, Jiaxin
%A Shi, Guangyuan
%A Wu, Xiao-Ming
%A Lam, Albert Y.S.
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F zhang-etal-2021-effectiveness-pre
%X 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.
%R 10.18653/v1/2021.findings-emnlp.96
%U https://aclanthology.org/2021.findings-emnlp.96
%U https://doi.org/10.18653/v1/2021.findings-emnlp.96
%P 1114-1120
Markdown (Informal)
[Effectiveness of Pre-training for Few-shot Intent Classification](https://aclanthology.org/2021.findings-emnlp.96) (Zhang et al., Findings 2021)
ACL
- Haode Zhang, Yuwei Zhang, Li-Ming Zhan, Jiaxin Chen, Guangyuan Shi, Xiao-Ming Wu, and Albert Y.S. Lam. 2021. Effectiveness of Pre-training for Few-shot Intent Classification. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1114–1120, Punta Cana, Dominican Republic. Association for Computational Linguistics.