This paper explores the application of Deep Neural Networks (DNN) and deep learning techniques in the field of machine translation, a branch of natural language processing (NLP). DNN has emerged as a fundamental component of machine learning algorithms. Recursive recurrent neural networks (R2NN), which combine recursive and recurrent neural networks such as Recursive auto encoder, are considered one of the most effective techniques for machine translation. This research focuses on training recurrent neural networks using semi-supervised learning techniques to reorder sentences from a source language to a target language. The study involves the utilization of Word2vec tool to create word vectors for the source language, and an Auto Encoder to reconstruct vectors for the destination language in a tree structure. The alignment of word vectors generated by word2vec plays a crucial role in the input vectors. Due to the complexity of the RNN structure, training the large word2vec data file can be time-consuming. Hence, powerful hardware support, such as GPUs, is required to expedite the training process and enhance system performance.
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