关键词: 机器翻译, 注意力机制, 编码器, 解码器, 循环神经网络, 长句翻译, 语义联系, 自然语言处理

Abstract:

The use of neural machine algorithms to translate English is a hot topic in current research. The ability to capture long-distance information in English translation using traditional sequence neural frameworks is too poor and has its own limitations. The current improved frameworks, such as recurrent neural network translation effect is not ideal. Aiming at the shortcomings of traditional machine translation algorithms, this paper establishes an attention coding and decoding model, combines the attention mechanism with a neural network framework, and uses TensorFlow to implement the entire English translation system. This method can improve the accuracy of translation. Experiments show that the BLUE value of the algorithm model constructed in the article has different degrees of improvement compared with traditional machine learning algorithms, which further proves the effectiveness of the improved attention mechanism model proposed in this article in English translation.

Key words: machine translation, attention mechanism, encoder, decoder, circulatory neural network, translation of long sentences, semantic connection, natural language processing