Deep Learning-Based English-Chinese Translation Research

Neural machine translation (NMT) has been bringing exciting news in the field of machine translation since its emergence. However, because NMT only employs single neural networks to convert natural languages, it suffers from two drawbacks in terms of reducing translation time: NMT is more sensitive to sentence length than statistical machine translation and the end-to-end implementation process fails to make explicit use of linguistic knowledge to improve translation performance. The network model performance of various deep learning machine translation tasks was constructed and compared in English-Chinese bilingual direction, and the defects of each network were solved by using an attention mechanism. The problems of gradient disappearance and gradient explosion are easy to occur in the recurrent neural network in the long-distance sequence. The short and long-term memory networks cannot reflect the information weight problems in long-distance sequences. In this study, through the comparison of examples, it is concluded that the introduction of an attention mechanism can improve the attention of context information in the process of model generation of the target language sequence, thus translating restore degree and fluency higher. This study proposes a neural machine translation method based on the divide-and-conquer strategy. Based on the idea of divide-and-conquer, this method identifies and extracts the longest noun phrase in a sentence and retains special identifiers or core words to form a sentence frame with the rest of the sentence. This method of translating the longest noun phrase and sentence frame separately by the neural machine translation system, and then recombining the translation, alleviates the poor performance of neural machine translation in long sentences. Experimental results show that the BLEU score of translation obtained by the proposed method has improved by 0.89 compared with the baseline method.