Deep neural networks are gaining increasing popularity for the
classic text classification task, due to their strong expressive power
and less requirement for feature engineering. Despite such attractiveness,
neural text classification models suffer from the lack of
training data in many real-world applications. Although many semisupervised
and weakly-supervised text classification models exist,
they cannot be easily applied to deep neural models and meanwhile
support limited supervision types. In this paper, we propose a
weakly-supervised method that addresses the lack of training data
in neural text classification. Our method consists of two modules:
(1) a pseudo-document generator that leverages seed information
to generate pseudo-labeled documents for model pre-training, and
(2) a self-training module that bootstraps on real unlabeled data for
model refinement. Our method has the flexibility to handle different
types of weak supervision and can be easily integrated into existing
deep neural models for text classification. We have performed extensive
experiments on three real-world datasets from different domains.
The results demonstrate that our proposed method achieves
inspiring performance without requiring excessive training data
and outperforms baseline methods significantly.
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LOPS: Learning Order Inspired Pseudo-Label Selection for Weakly Supervised Text Classification
Weakly supervised text classification methods typically train a deep neural classifier based on pseudo-labels. The quality of pseudo-labels is crucial to final performance but they are inevitably noisy due to their heuristic nature, so selecting the correct ones has a huge potential for performance boost. One straightforward solution is to select samples based on the softmax probability scores in the neural classifier corresponding to their pseudo-labels. However, we show through our experiments that such solutions are ineffective and unstable due to the erroneously high-confidence predictions from poorly calibrated models. Recent studies on the memorization effects of deep neural models suggest that these models first memorize training samples with clean labels and then those with noisy labels. Inspired by this observation, we propose a novel pseudo-label selection method LOPS that takes learning order of samples into consideration. We hypothesize that the learning order reflects the probability of wrong annotation in terms of ranking, and therefore, propose to select the samples that are learnt earlier. LOPS can be viewed as a strong performance-boost plug-in to most existing weakly-supervised text classification methods, as confirmed in extensive experiments on four real-world datasets.
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- Award ID(s):
- 2040727
- NSF-PAR ID:
- 10403515
- Date Published:
- Journal Name:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Page Range / eLocation ID:
- 4894–4908
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
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