Establishment of machine translation model based on asymmetric Encoder-Decoder structure and its application in English grammar error detection

Abstract

Asymmetric codecs have been successful in many deep learning tasks recently. It is a forward-looking research to apply this deep learning neural network to English grammar error detection tasks. The traditional machine translation model has poor semantic analysis ability, which leads to the low accuracy of the results when using the model to detect English grammatical errors. To solve this problem, this paper designs a deep learning neural network model based on asymmetric Encoder-Decoder. Firstly, English sentences need to be pre-processed and converted into a tuple probability table recognizable by the model, which is subsequently fed into the model of the encoder decoder. In the encoder: Pre-embedding is first implemented with the help of temporal convolutional network infrastructure to initially obtain the word mapping relationships in the sentences and then connected to BiLSTM for accurate word embedding to capture the word-to-word relationships in the sentences. In addition, for the context intermediate vector output by the encoder, in order to more accurately and adaptively extract relevant features with global information, the attention mechanism is used to process the features output from bilstm according to their corresponding weights. In the decoder, bigru is used to decode the context intermediate vector output from the encoder. And outputs the translation result after the decoding is completed. After training the machine translation model, word analysis is performed through the process of decoding the model. Finally, the experiment detected English grammatical errors, such as articles, prepositions, nouns, verbs and subject predicate agreement. The experimental results show that the designed model outperforms the comparison method in terms of evaluation metrics such as precision, recall and F1 values for English grammar detection. The necessity of each component of the model is verified, and it is shown that the m odel can effectively improve the accuracy of English grammar error detection. The research in this paper provides an important theoretical guidance for applying deep learning neural networks with asymmetric codecs to English grammar error detection.