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Comprehensive Study on Zero-Shot Text Classification Using Category Mapping
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DOI：10.1109/ACCESS.2025.3538103.png)
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Existing zero-shot text classification methods based on large pre-trained models with added prompts exhibit strong representational capacity and scalability but have relatively poor commercial applicability. Approaches that fine-tune smaller models using label mappings and existing datasets for zero-shot classification are simpler but suffer from weaker generalization capabilities. This paper employs three strategies to improve the accuracy and generalization of pre-trained models in zero-shot text classification tasks: 1) Utilizing a pre-trained model that transforms inputs into a standardized multiple-choice format. 2) Constructing a text classification training set using Wikipedia text data to fine-tune the pre-trained model; 3) Proposing a zero-shot category mapping method based on GloVe text similarity, using Wikipedia categories as substitutes for text labels. Without fine-tuning on the target labels, this method achieves performance comparable to the best models fine-tuned with target labels.
Keyword:
Vectors
Context modeling
Text categorization
Training
Encoding
Bidirectional control
Computational modeling
Data models
Predictive models
Decoding
Natural language processing
pre-trained language model
zero-shot text classification
classification
GloVe
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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