Why does a neural machine translation (NMT) system, despite achieving a high BLEU score, still sometimes produce translations that are semantically inaccurate or nonsensical, especially with idiomatic expressions?

💡 Explanation

NMT systems can achieve high BLEU scores by capturing surface-level patterns; however, they often lack deeper semantic understanding. Because NMT relies on statistical correlations without human-like comprehension, it struggles with nuances such as idioms. Therefore, limited context hinders accurate idiomatic translation, rather than insufficient surface form lexicons.

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