Guide-linked quiz · Medium Level
Machine Translation Quiz
Check translation data alignment, tokenization, BLEU limits, language-pair evaluation, and semantic errors such as changed numbers or negation.
Question 1 of 8
What does parallel training data contain?
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- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
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