Abstract
With the wide use of computers, machine translation has been gradually applied in many fields from natural language processing, such as industry, education, and so on. Due to the increasing demand for multilanguage translation, it is an urgent problem to effectively improve the quality of text translation. Driven by the upsurge of artificial intelligence, neural network technology is increasingly integrated into the field of machine translation, which gradually expands the traditional machine translation method into neural machine translation method. With the continuous improvement of deep learning technology, machine translation has gradually integrated these methods and strategies and achieved good results in multiple tasks, but there are still some shortcomings. The most prominent problem is that, since word vector is the basis for the model to obtain semantic and grammatical information, the existing methods cannot obtain semantic and grammatical feature information, which greatly reduces the accuracy of English translation. Based on this, this paper proposed a method of splicing word vector with character- level and word-level encoding vector. The characterization of fusion of more word vector can effectively solve the word does not appear in the table, the word with some low frequency, can express meaning more complete information, performance directly affects the whole translation model, the results can be seen through the experiment, we put forward the characteristics of the fusion method and strategy, can effectively enhance the overall translation performance of the model.
Highlights
With the widespread use of computers, the speed of manual text translation is no longer enough to meet daily needs [1,2,3]. erefore, how to use computers to realize the mutual conversion between multiple languages is an urgent problem to be solved
CovS2S: is model is a neural translation model proposed by Facebook based on convolutional neural network, which preserves the long-distance dependence of words in sentences through gating mechanism combined with multihop convolution and solves the problem of gradient descent disappearing in the training process. is model relies on the pattern of recurrent neural network for neural translation break, improve the model training parallel ability, and model training efficiency in the translation effect is very good
Deep-Att + posUnk: is model is a neural translation model proposed by Facebook based on convolutional neural network, which preserves the long-distance dependence of words in sentences through gating mechanism combined with multihop convolution and solves the problem of gradient descent disappearing in the training process. is model relies on the pattern of recurrent neural network for neural translation break and improve the model training parallel ability, and model training efficiency in the translation effect is very good
Summary
With the widespread use of computers, the speed of manual text translation is no longer enough to meet daily needs [1,2,3]. erefore, how to use computers to realize the mutual conversion between multiple languages is an urgent problem to be solved. Driven by the upsurge of artificial intelligence, improving the quality of machine translation by using artificial intelligence technology is a hot research direction in the field of natural language processing. Since the 1890s, data-driven statistical machine translation has replaced rule-driven method and gradually become the mainstream machine translation method. Statistical machine translation has become the core technology of online machine translation systems at home and abroad because of its advantages such as low labor cost, short development time, Scientific Programming and good robustness, which overcomes the bottleneck of rule-making based translation methods. The traditional statistical machine translation technology still faces severe challenges due to the serious problems of data sparsity, strong dependence on corpus, and too much time cost [6]. Syntactic analysis translation methods can be roughly divided into two categories: language templatebased translation methods and statistics-based translation methods. e translation method based on language template, which refers to the surface features of a sentence, is the earliest technical method of grammar translation. e advantage of language template-based translation method is that the translation is most accurate when the sentence template features are strong, while the disadvantage is that the translation is inaccurate or even impossible when the sentence template features are weak [7,8,9]
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