BLEU: a Method for Automatic Evaluation of Machine Translation
Papineni et al., 2002, BLEU (Proceedings of the 40th Annual Meeting of ACL)
Subscriber deck — free during early access
3 cards in “3 The BLEU evaluation” · whole deck
3 The BLEU evaluation
- Why can’t BLEU scores be compared across test sets with different numbers of references?More references give higher scores: on the same test corpus a human translator scored about 0.35 against four references but 0.26 against two. Even a human translation seldom scores 1.
- Papineni et al., 2002, BLEU (Proceedings of the 40th Annual Meeting of ACL), §3
- How did Papineni et al. check whether BLEU differences between systems are reliable?They split the 500-sentence test corpus into 20 blocks of 25 sentences, computed BLEU per block, and used paired t-statistics between systems; the differences were statistically very significant.
- Papineni et al., 2002, BLEU (Proceedings of the 40th Annual Meeting of ACL), §3
- Do you need multiple reference translations for BLEU?Not necessarily: using a single randomly chosen reference per story (for stylistic variety) kept the same system ranking, suggesting a big test corpus with one reference each can work if references aren’t all from the same translator.
- Papineni et al., 2002, BLEU (Proceedings of the 40th Annual Meeting of ACL), §3
Source
- Kishore Papineni, Salim Roukos, Todd Ward and Wei-Jing Zhu. 2002. BLEU: a Method for Automatic Evaluation of Machine Translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318. https://aclanthology.org/P02-1040/