Survey of Research on Curriculum Learning in Neural Machine Translation
Curriculum learning, as an emerging technology, has gradually attracted attention in recent years. It is in line with human learning habits, from simple to difficult, from shallow to deep. Its core idea is to allow the model to learn from simple and basic concepts, and gradually transition to more c...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2025-02-01
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| Series: | Jisuanji kexue yu tansuo |
| Subjects: | |
| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2403008.pdf |
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| Summary: | Curriculum learning, as an emerging technology, has gradually attracted attention in recent years. It is in line with human learning habits, from simple to difficult, from shallow to deep. Its core idea is to allow the model to learn from simple and basic concepts, and gradually transition to more complex and higher-level content. In the translation of neural machines, curriculum learning is a training strategy to help models learn in accordance with certain laws. Curriculum learning has been proven to accelerate the convergence of the model and improve the quality and stability of the model translation. This paper first introduces the definition and basic framework of curriculum learning from the perspective of machine learning, and further explores its application in the field of neural machine translation. Two approaches of curriculum learning, namely predefined curriculum learning and dynamic curriculum learning, are discussed in detail from the perspectives of sample difficulty evaluation and model training scheduling strategies. Predefined curriculum learning guides the model to gradually learn tasks from simple to complex by pre-determining the difficulty order of samples. In contrast, dynamic curriculum learning adjusts the difficulty of samples dynamically based on the model’s current learning state, offering a more flexible training approach. Additionally, this paper analyzes the future research trends of curriculum learning in neural machine translation and proposes three promising research directions. |
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| ISSN: | 1673-9418 |