Cuestionario vinculado a una guía · Medio Nivel

Prueba de traducción automática

Verifique la alineación de los datos de traducción, la tokenización, los límites BLEU, la evaluación de pares de idiomas y errores semánticos como números modificados o negación.

Rutas de guía relacionadasTraducción automática
Pregunta 1 de 8

What does parallel training data contain?

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Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

  1. What does parallel training data contain?
    • Source passages paired with corresponding translations
    • Unrelated documents grouped by font
    • Only target-language references
    • A list of model predictions without sources
  2. What can mismatched source-target alignment teach a translation model?
    • A guaranteed improvement in fluency
    • The wrong relationship between passages
    • A correct translation for every language
    • A better BLEU reference
  3. Why keep related documents together when splitting evaluation data?
    • To remove the need for references
    • To make tokenization identical
    • To reduce near-duplicate leakage across splits
    • To increase sentence length
  4. A tokenizer splits one writing system into many more units. What should evaluators check?
    • Whether source and target use the same alphabet
    • Only the model’s name
    • Whether BLEU is always high
    • Length limits and whether either passage is truncated
  5. What does BLEU compare?
    • Whether a translation is legally valid
    • Patterns of word sequences with reference translations
    • The full meaning and readability of a passage
    • The number of languages in a model
  6. A candidate translation changes “did not arrive” to “did arrive.” What error should reviewers flag?
    • An insertion that cannot matter
    • A casing normalization choice
    • Incorrect negation that reverses meaning
    • A target-language tokenization win
  7. A model has a strong average score across several well-resourced languages. What could that conceal?
    • Poor performance in a less-represented language or specialized domain
    • That all language pairs are equally reliable
    • That there are no terminology errors
    • That the test set has no duplicates
  8. What should accompany an automatic metric in translation evaluation?
    • A single score without settings
    • Only a larger reference corpus
    • An assumption that meaning is preserved
    • A documented setup and bilingual review of specific errors