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BLEU score

As explained in Deep Learning with R, Third Edition

A BLEU score measures the similarity of machine-translated text to one or more high-quality reference translations using n-gram overlap.

A BLEU score evaluates machine translation by comparing generated text with one or more high-quality reference translations. Unlike next-token accuracy, it accommodates slightly misaligned sequences and better reflects inference, where the decoder generates the target sentence from scratch. section 15.2.2

BLEU is useful because it:

  • Compares machine-translated text with reference translations. section 15.2.2
  • Tolerates wording whose sequence is slightly misaligned. section 15.2.2
  • Addresses the limitations of validation accuracy for autoregressive translation. section 15.2.2

Translation evaluation

A model might generate “te lo voy mañana a decir” for “I will tell you tomorrow,” while a reference uses a different word order. BLEU is designed to assess similarity without requiring exact token-by-token agreement. section 15.2.2

How Deep Learning with R, Third Edition covers it

How deep it goes
Defines BLEU and motivates its use but stops short of explaining its n-gram calculation or interpreting score values.
Examples
Uses English-to-Spanish test translations, including reordered or erroneous outputs, to illustrate real inference behavior.
What you can do after
Evaluate machine-translation outputs with a metric more appropriate than teacher-forced next-token accuracy.
How it connects
Connects BLEU to machine translation, sequence-to-sequence models, autoregressive decoding, reference translations, and next-token accuracy.

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This explainer was generated by Manning's knowledge graph from the text of Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.