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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 5, 2024.
Abstract: This paper discusses the main challenges and solution strategies of low-resource machine translation, and proposes a novel translation method combining migration learning and multi-view training. In a low-resource environment, neural machine translation models are prone to problems such as insufficient generalization performance, inaccurate translation of long sentences, difficulty in processing unregistered words, and inaccurate translation of domain-specific terms due to their heavy reliance on massively parallel corpora. Migration learning gradually adapts to the translation tasks of low-resource languages in the process of fine-tuning by borrowing the general translation knowledge of high-resource languages and utilizing pre-training models such as BERT, XLM-R, and so on. Multi-perspective training, on the other hand, emphasizes the integration of source and target language features from multiple levels, such as word level, syntax and semantics, in order to enhance the model's comprehension and translation ability under limited data conditions. In the experiments, the study designed an experimental scheme containing pre-training model selection, multi-perspective feature construction, and migration learning and multi-perspective fusion, and compared the performance with randomly initialized Transformer model, pre-training-only model, and traditional statistical machine translation model. The experiments demonstrate that the model with multi-view training strategy significantly outperforms the baseline model in evaluation metrics such as BLEU, TER, and ChrF, and exhibits stronger robustness and accuracy in processing complex language structures and domain-specific terminology.
Jing Yan, Tao Lin and Shuai Zhao, “Migration Learning and Multi-View Training for Low-Resource Machine Translation” International Journal of Advanced Computer Science and Applications(IJACSA), 15(5), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150572
@article{Yan2024,
title = {Migration Learning and Multi-View Training for Low-Resource Machine Translation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150572},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150572},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {5},
author = {Jing Yan and Tao Lin and Shuai Zhao}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.