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Neural Machine Translation Robustness.pdf

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Preview of Neural Machine Translation Robustness
🔗 Source: aclanthology.org
📊 Size: 1.14 MB
📄 Pages: 20 pages
⬇️ Downloads: 220

Summary

Neural machine translation (NMT) is more robust to language imbalance in tokenizer training than expected, with performance drops only occurring when language data ratios are highly disparate (e.g., 1:105), especially when languages share scripts. Better performance is often achieved with more balanced languages, and two features (UNK rate and closeness to character level) can indicate poor translation performance before performing the task. NMT is more sensitive to language imbalance in model training than in tokenizer training. Practical suggestions include keeping languages balanced when training a new multilingual tokenizer and evaluating a pretrained tokenizer on a development set to ensure low UNK rates and closeness to character level.

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Description

Neural machine translation (NMT) is more robust to language imbalance in tokenizer training than expected, with performance drops only occurring when language...

Technical Information

  • File Format: PDF
  • File Size: 1.14 MB
  • Pages: 20
  • Language: EN
  • Total Downloads: 220
  • Last Updated: 1 week ago

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