Neural Machine Translation
Summary
Neural Machine Translation (NMT) is an advanced approach to automated translation that aims to bridge the gap between human and machine translation capabilities. As outlined in Google’s research on their Neural Machine Translation System (GNMT), NMT utilizes deep learning techniques to overcome limitations of traditional phrase-based systems. The GNMT model employs a deep LSTM network with attention mechanisms and residual connections, addressing challenges such as computational expense, translation speed, and rare word handling. Key innovations include the use of low-precision arithmetic for faster inference, sub-word units (wordpieces) for improved rare word translation, and specialized beam search techniques for better coverage and length normalization. These advancements have resulted in significant improvements in translation quality, with GNMT demonstrating competitive performance on benchmark tests and reducing translation errors by an average of 60% compared to previous production systems.