Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled Sets
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866917557440610304 |
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| author | Karisani, Payam |
| author_facet | Karisani, Payam |
| contents | We propose a semi-supervised text classifier based on self-training using one positive and one negative property of neural networks. One of the weaknesses of self-training is the semantic drift problem, where noisy pseudo-labels accumulate over iterations and consequently the error rate soars. In order to tackle this challenge, we reshape the role of pseudo-labels and create a hierarchical order of information. In addition, a crucial step in self-training is to use the classifier confidence prediction to select the best candidate pseudo-labels. This step cannot be efficiently done by neural networks, because it is known that their output is poorly calibrated. To overcome this challenge, we propose a hybrid metric to replace the plain confidence measurement. Our metric takes into account the prediction uncertainty via a subsampling technique. We evaluate our model in a set of five standard benchmarks, and show that it significantly outperforms a set of ten diverse baseline models. Furthermore, we show that the improvement achieved by our model is additive to language model pretraining, which is a widely used technique for using unlabeled documents. Our code is available at https://github.com/p-karisani/RST. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_00575 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled Sets Karisani, Payam Computation and Language We propose a semi-supervised text classifier based on self-training using one positive and one negative property of neural networks. One of the weaknesses of self-training is the semantic drift problem, where noisy pseudo-labels accumulate over iterations and consequently the error rate soars. In order to tackle this challenge, we reshape the role of pseudo-labels and create a hierarchical order of information. In addition, a crucial step in self-training is to use the classifier confidence prediction to select the best candidate pseudo-labels. This step cannot be efficiently done by neural networks, because it is known that their output is poorly calibrated. To overcome this challenge, we propose a hybrid metric to replace the plain confidence measurement. Our metric takes into account the prediction uncertainty via a subsampling technique. We evaluate our model in a set of five standard benchmarks, and show that it significantly outperforms a set of ten diverse baseline models. Furthermore, we show that the improvement achieved by our model is additive to language model pretraining, which is a widely used technique for using unlabeled documents. Our code is available at https://github.com/p-karisani/RST. |
| title | Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled Sets |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2401.00575 |