Neural machine translation (NMT) faces several challenges compared to traditional statistical machine translation (SMT). These include domain mismatch, training data quantity, rare words, long sentences, word alignment, and beam search. NMT systems often perform worse out-of-domain and under low-resource conditions, sacrificing adequacy for fluency. They require more training data to achieve high quality, especially in low-resource settings. NMT performs better on rare words with sub-word encoding but struggles with highly inflected words. Long sentences are translated less accurately by NMT, though performance improves up to about 60 words. The attention model in NMT does not always align words as expected, and beam search decoding improves quality only with narrow beams. NMT systems are less interpretable, making it difficult to understand why certain word choices are made. The paper compares NMT and SMT systems on various language pairs and datasets, showing that NMT has a steeper learning curve and performs better with large data. However, it struggles with rare words and long sentences. The study highlights the need for further research to improve NMT's performance in these areas.Neural machine translation (NMT) faces several challenges compared to traditional statistical machine translation (SMT). These include domain mismatch, training data quantity, rare words, long sentences, word alignment, and beam search. NMT systems often perform worse out-of-domain and under low-resource conditions, sacrificing adequacy for fluency. They require more training data to achieve high quality, especially in low-resource settings. NMT performs better on rare words with sub-word encoding but struggles with highly inflected words. Long sentences are translated less accurately by NMT, though performance improves up to about 60 words. The attention model in NMT does not always align words as expected, and beam search decoding improves quality only with narrow beams. NMT systems are less interpretable, making it difficult to understand why certain word choices are made. The paper compares NMT and SMT systems on various language pairs and datasets, showing that NMT has a steeper learning curve and performs better with large data. However, it struggles with rare words and long sentences. The study highlights the need for further research to improve NMT's performance in these areas.