Machine translation using deep learning: An overview

Citations Over TimeTop 10% of 2017 papers

Abstract

This Paper reveals the information about Deep Neural Network (DNN) and concept of deep learning in field of natural language processing i.e. machine translation. Now day's DNN is playing major role in machine leaning technics. Recursive recurrent neural network (R2NN) is a best technic for machine learning. It is the combination of recurrent neural network and recursive neural network (such as Recursive auto encoder). This paper presents how to train the recurrent neural network for reordering for source to target language by using Semi-supervised learning methods. Word2vec tool is required to generate word vectors of source language and Auto encoder helps us in reconstruction of the vectors for target language in tree structure. Results of word2vec play an important role in word alignment of the input vectors. RNN structure is very complicated and to train the large data file on word2vec is also a time-consuming task. Hence, a powerful hardware support (GPU) is required. GPU improves the system performance by decreasing training time period.

Related Papers

• → Support vector machines and Word2vec for text classification with semantic features(2015)457 cited
• → Review on Word2Vec Word Embedding Neural Net(2020)73 cited
• → Dynamic Frame Skipping for Fast Speech Recognition in Recurrent Neural Network Based Acoustic Models(2018)10 cited
• → A new recurrent neural network architecture for pattern recognition(1996)8 cited
• → Analyzing Semantic Relations of Word Vectors trained by The Word2vec Model(2019)1 cited