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Abstract
Under the current artificial intelligence boom, machine translation is a research direction of natural language processing, which has important scientific research value and practical value. In practical applications, the variability of language, the limited capability of representing semantic information, and the scarcity of parallel corpus resources all constrain machine translation towards practicality and popularization. In this paper, we conduct deep mining of source language text data to express complex, high‐level, and abstract semantic information using an appropriate text data representation model; then, for machine translation tasks with a large amount of parallel corpus, I use the capability of annotated datasets to build a more effective migration learning‐based end‐to‐end neural network machine translation model on a supervised algorithm; then, for machine translation tasks with parallel corpus data resource‐poor language machine translation tasks, migration learning techniques are used to prevent the overfitting problem of neural networks during training and to improve the generalization ability of end‐to‐end neural network machine translation models under low‐resource conditions. Finally, for language translation tasks where the parallel corpus is extremely scarce but monolingual corpus is sufficient, the research focuses on unsupervised machine translation techniques, which will be a future research trend.
Highlights
With the rapid development of the Internet as well as information technology, the field of artificial intelligence is gaining more and more attention, attracting a large number of researchers and developers
Machine translation is a hot spot of research in the field of artificial intelligence, with important theoretical significance and great application value
Mainstream neural network models that can be used by endto-end neural network machine translation systems based on sequence-to-sequence transformation models, encoders, and decoders include recurrent neural networks and improved long- and short-term memory networks and gated recurrent networks, etc.; convolutional neural network models; and translation models based on attention mechanisms
Summary
With the rapid development of the Internet as well as information technology, the field of artificial intelligence is gaining more and more attention, attracting a large number of researchers and developers. The domain-adaptive approach of migration learning, on the other hand, can use the high-resource parallel corpus data to extract the useful information that may be used in the low-resource parallel corpus learning. The domain-adaptive approach of migration learning can use the high-resource parallel corpus data to extract the useful information that may be used in lowresource parallel corpus learning. This paper proposes an intelligent English translation model based on neural network migration learning, which can construct feature mapping relations among pretrained language models and search for matching relations in the high-dimensional feature space, which makes good use of the common relations among languages and can use the existing knowledge space to save the resources required for model learning, so it has important research significance and practical value
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