Abstract
In order to reduce the workload of manual grading and improve the efficiency of grading, a computerized intelligent grading system for English translation based on natural language processing is designed. An attention-embedded LSTM English machine translation model is proposed. Firstly, according to the characteristics of the standard LSTM network model that uses fixed dimensional vectors to represent words in the encoding stage, an English machine translation model based on LSTM attention embedding is established; the structure level of the English translation scoring system is constructed. A linguistic model of the English translation scoring system is established, and the probability distribution of a particular sentence sequence or word sequence of the translated text is statistically calculated using the model. The results show that the English machine translation model based on LSTM attention embedding proposed in this study can enhance the representation of the source language contextual information and improve the performance of the English machine translation model and the quality of the translation compared with the English machine translation models constructed by existing neural network structures, such as standard LSTM models, RNN models, and GRU-Attention translation models.
Highlights
In the literature [6], an English translation scoring system based on hidden Markov model is used, combining Markov model and Viterbi comparison system to input similar words between the translation and the reference translation, match the similar words to calculate the proximity between them, and compare the similarity between the translated utterances, and according to the comparison results, achieve the translation scoring [7]. e accuracy of the scoring results of this system is high, but the computation is large and time-consuming
Translation models such as those based on LSTM, RNN, and GRU-Attention neural networks have been widely used in the field of English machine translation [8–11] using neural networks with different structures to study the translation effect of English machine translation in the field of component products and other areas and to Scientific Programming achieve intelligent English machine translation. e results of English machine translation in areas such as component products were studied using different structures of neural networks, and intelligent English machine translation was achieved
The abovementioned English machine translation models based on neural network structures all suffer from the problem of unsatisfactory translation results due to the loss of long-distance information in the process of transmission due to long-distance dependence and need to be improved [12]
Summary
With the development of computer technology and the maturity of artificial intelligence technology, machine translation is gradually replacing human translation and occupying a larger proportion in the translation field. E scoring results of this system have large errors and the process of word collocation analysis is complicated. Translation models such as those based on LSTM, RNN, and GRU-Attention neural networks have been widely used in the field of English machine translation [8–11] using neural networks with different structures to study the translation effect of English machine translation in the field of component products and other areas and to Scientific Programming achieve intelligent English machine translation. The abovementioned English machine translation models based on neural network structures all suffer from the problem of unsatisfactory translation results due to the loss of long-distance information in the process of transmission due to long-distance dependence and need to be improved [12]
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