BLEU¶
Why this mattered¶
TBD
Abstract¶
Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.
Related¶
- enables → Effective Approaches to Attention-based Neural Machine Translation — BLEU provided the automatic machine-translation metric used to evaluate the attention-based neural translation models.
- enables → Show and tell: A neural image caption generator — BLEU provided an automatic n-gram translation metric that Show and Tell adapted to evaluate generated image captions.
- cite ← Effective Approaches to Attention-based Neural Machine Translation — The attention-based NMT paper reports translation quality using the BLEU automatic machine-translation evaluation metric.
- cite ← Show and tell: A neural image caption generator — Show and Tell evaluates generated captions using the BLEU machine-translation precision metric.