New Intent Discovery with Pre-training and Contrastive Learning
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arXiv
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| Format: | Preprint |
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2022
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| _version_ | 1866909567551537152 |
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| author | Zhang, Yuwei Zhang, Haode Zhan, Li-Ming Lam, Albert Y. S. Wu, Xiao-Ming |
| author_facet | Zhang, Yuwei Zhang, Haode Zhan, Li-Ming Lam, Albert Y. S. Wu, Xiao-Ming |
| contents | New intent discovery aims to uncover novel intent categories from user utterances to expand the set of supported intent classes. It is a critical task for the development and service expansion of a practical dialogue system. Despite its importance, this problem remains under-explored in the literature. 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. In this paper, we provide new solutions to two important research questions for new intent discovery: (1) how to learn semantic utterance representations and (2) how to better cluster utterances. Particularly, we first propose a multi-task pre-training strategy to leverage rich unlabeled data along with external labeled data for representation learning. Then, we design a new contrastive loss to exploit self-supervisory signals in unlabeled data for clustering. Extensive experiments on three intent recognition benchmarks demonstrate the high effectiveness of our proposed method, which outperforms state-of-the-art methods by a large margin in both unsupervised and semi-supervised scenarios. The source code will be available at https://github.com/zhang-yu-wei/MTP-CLNN. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_12914 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | New Intent Discovery with Pre-training and Contrastive Learning Zhang, Yuwei Zhang, Haode Zhan, Li-Ming Lam, Albert Y. S. Wu, Xiao-Ming Computation and Language New intent discovery aims to uncover novel intent categories from user utterances to expand the set of supported intent classes. It is a critical task for the development and service expansion of a practical dialogue system. Despite its importance, this problem remains under-explored in the literature. 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. In this paper, we provide new solutions to two important research questions for new intent discovery: (1) how to learn semantic utterance representations and (2) how to better cluster utterances. Particularly, we first propose a multi-task pre-training strategy to leverage rich unlabeled data along with external labeled data for representation learning. Then, we design a new contrastive loss to exploit self-supervisory signals in unlabeled data for clustering. Extensive experiments on three intent recognition benchmarks demonstrate the high effectiveness of our proposed method, which outperforms state-of-the-art methods by a large margin in both unsupervised and semi-supervised scenarios. The source code will be available at https://github.com/zhang-yu-wei/MTP-CLNN. |
| title | New Intent Discovery with Pre-training and Contrastive Learning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2205.12914 |