Google's Neural Machine Translation (GNMT) system addresses key challenges in neural machine translation (NMT), including computational efficiency, robustness to rare words, and translation accuracy. GNMT uses a deep LSTM network with 8 encoder and 8 decoder layers, incorporating residual and attention connections. To improve parallelism and reduce training time, the attention mechanism connects the bottom decoder layer to the top encoder layer. Low-precision arithmetic is used during inference to accelerate translation speed. Rare words are handled using sub-word units ("wordpieces"), balancing flexibility and efficiency. Beam search with length normalization and coverage penalty enhances translation quality. GNMT achieves competitive results on WMT'14 benchmarks, outperforming Google's phrase-based system by 60% in human evaluations. The system uses model parallelism and data parallelism to speed up training, with a shared wordpiece model for both source and target languages. Quantized inference with reduced precision arithmetic improves efficiency on TPUs. The model uses a combination of maximum-likelihood training and reinforcement learning for refinement. GNMT's decoder employs beam search with length normalization and coverage penalty to improve translation accuracy. Experiments show that GNMT achieves high BLEU scores on WMT'14 datasets, with optimal parameters for length normalization and coverage penalty. The system demonstrates improved accuracy and efficiency across multiple language pairs, including English-French, English-German, and English-Chinese.Google's Neural Machine Translation (GNMT) system addresses key challenges in neural machine translation (NMT), including computational efficiency, robustness to rare words, and translation accuracy. GNMT uses a deep LSTM network with 8 encoder and 8 decoder layers, incorporating residual and attention connections. To improve parallelism and reduce training time, the attention mechanism connects the bottom decoder layer to the top encoder layer. Low-precision arithmetic is used during inference to accelerate translation speed. Rare words are handled using sub-word units ("wordpieces"), balancing flexibility and efficiency. Beam search with length normalization and coverage penalty enhances translation quality. GNMT achieves competitive results on WMT'14 benchmarks, outperforming Google's phrase-based system by 60% in human evaluations. The system uses model parallelism and data parallelism to speed up training, with a shared wordpiece model for both source and target languages. Quantized inference with reduced precision arithmetic improves efficiency on TPUs. The model uses a combination of maximum-likelihood training and reinforcement learning for refinement. GNMT's decoder employs beam search with length normalization and coverage penalty to improve translation accuracy. Experiments show that GNMT achieves high BLEU scores on WMT'14 datasets, with optimal parameters for length normalization and coverage penalty. The system demonstrates improved accuracy and efficiency across multiple language pairs, including English-French, English-German, and English-Chinese.