Native language identification from text using a fine-tuned GPT-2 model
Native language identification (NLI) is a critical task in computational linguistics, supporting applications such as personalized language learning, forensic analysis, and machine translation. This study investigates the use of a fine-tuned GPT-2 model to enhance NLI accuracy. Using the NLI-PT data...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-05-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-2909.pdf |
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| Summary: | Native language identification (NLI) is a critical task in computational linguistics, supporting applications such as personalized language learning, forensic analysis, and machine translation. This study investigates the use of a fine-tuned GPT-2 model to enhance NLI accuracy. Using the NLI-PT dataset, we preprocess and fine-tune GPT-2 to classify the native language of learners based on their Portuguese-written texts. Our approach leverages deep learning techniques, including tokenization, embedding extraction, and multi-layer transformer-based classification. Experimental results show that our fine-tuned GPT-2 model significantly outperforms traditional machine learning methods (e.g., SVM, Random Forest) and other pre-trained language models (e.g., BERT, RoBERTa, BioBERT), achieving a weighted F1 score of 0.9419 and an accuracy of 94.65%. These results show that large transformer models work well for native language identification and can help guide future research in personalized language tools and artificial intelligence (AI)-based education. |
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| ISSN: | 2376-5992 |