Deep Learning Techniques for Machine Translation: A Survey

Machine Translation (MT) is a crucial sub-field of Artificial Intelligence (AI). Since its inception in the mid-20th century, it tackles the intricate challenge of translating text across languages. MT has seen remarkable progress, particularly over the past decade with the advent of deep learning (DL) techniques. Key DL methods include feed-forward deep neural networks (NN), convolutional NN (CNN), recurrent NN (RNN), long term/ short-term memory networks (LSTM), Gated recurrent units (GRU), attention mechanisms, Transformer model and autoencoders. These advancements have enabled the widespread use of MT in web-based translation services, mobile applications, and various MT platforms. Each addressing different challenges and excelling in various aspects of MT. This research offers a thorough review of DL techniques in MT, identifying the most utilized MT systems, their architectures, and performance outcomes. It also highlights the merits, and limitations of these methods. The findings reveal that DL techniques for MT using transform model is the prevailing paradigm. The research concludes with a discussion on potential future research directions.