Abstract
The use of neural machine algorithms for English translation is a hot topic in the current research. English translation using the traditional sequential neural framework, which is too poor at capturing long-distance information, has its own major limitations. However, the current improved frameworks, such as recurrent neural network translation, are not satisfactory either. In this paper, we establish an attention coding and decoding model to address the shortcomings of traditional machine translation algorithms, combine the attention mechanism with a neural network framework, and implement the whole English translation system based on TensorFlow, thus improving the translation accuracy. The experimental test results show that the BLUE values of the algorithm model built in this paper are improved to different degrees compared with the traditional machine learning algorithms, which proves that the performance of the proposed algorithm model is significantly improved compared with the traditional model.
Highlights
Natural language is an important vehicle for knowledge and information dissemination, as well as an outward expression of human civilization and wisdom [1]
As the Internet continues to evolve and the era of artificial intelligence approaches, the amount of data generated on the Internet is growing exponentially and how to efficiently process and extract useful data has become an urgent problem for major companies [4]
Language is the main tool for cultural exchange, but there is a huge language gap between the mother tongues of different countries, which undoubtedly brings many obstacles to the cultural exchange of people in the world [5, 6]. erefore, the demand for human translators is increasing, and this has led to the high price of human translators, which is difficult for the general public, to afford such an expensive price. e automated nature of machine translation makes translation between languages easy and efficient, which can undoubtedly contribute to a wide range of communication between countries around the world
Summary
Natural language is an important vehicle for knowledge and information dissemination, as well as an outward expression of human civilization and wisdom [1]. Neural machine translation (NMT), based on deep learning, is capable of automatically learning abstract features and establishing relationships between source and target utterances and has recently obtained far better performance than SMT in various tasks of machine translation [11,12,13,14]. Taking neural network as the basic unit to construct the corresponding machine translation model, using the ability that neural network can automatically learn abstract features and establish the mapping relationship between input signal and output signal, with the support of large-scale corpus, the performance of neural machine translation has reached an unprecedented height and the performance on various machine translation tasks is far more than that of statistical machine translation [20], which becomes the dominant research approach. Internet companies such as Baidu, Netease, Tencent, KDXunfei, and Sogou are increasing their research on machine translation, which has given birth to translation software such as Baidu Translator, Sogou Translator, and Youdao Translator and real-time voice translation tools such as Xunfei Translator [26, 27]
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