NLTK: Corpus-Level BLEU vs Sentence-Level BLEU Score
BLEU (Bilingual Evaluation Understudy) is a widely used metric for evaluating the quality of machine translation. It measures the similarity between candidate translations and reference translations. NLTK, a popular Python library for natural language processing, provides functions to calculate both corpus-level and sentence-level BLEU scores.
Corpus-Level BLEU Score
The corpus-level BLEU score is calculated by averaging the sentence-level BLEU scores over all the sentences in a corpus. It provides an overall evaluation of the translation quality across the entire dataset.
Example
from nltk.translate.bleu_score import corpus_bleu from nltk.translate.bleu_score import sentence_bleu # Reference translations references = [[['This', 'is', 'a', 'test', 'sentence', '.']]] # Candidate translations candidates = [[['This', 'is', 'a', 'test', 'sentence', '.']]] # Calculate the corpus-level BLEU score corpus_bleu_score = corpus_bleu(references, candidates) # Print the score print(corpus_bleu_score)
1.0
The output shows that the corpus-level BLEU score is 1.0, indicating a perfect match between the candidate and reference translations.
Sentence-Level BLEU Score
The sentence-level BLEU score is calculated for a single sentence pair. It measures the similarity between a candidate translation and a reference translation for that specific sentence.
Example
from nltk.translate.bleu_score import sentence_bleu # Reference translation reference = ['This', 'is', 'a', 'test', 'sentence', '.'] # Candidate translation candidate = ['This', 'is', 'a', 'test', 'sentence', '.'] # Calculate the sentence-level BLEU score sentence_bleu_score = sentence_bleu([reference], candidate) # Print the score print(sentence_bleu_score)
1.0
The output shows that the sentence-level BLEU score is 1.0, indicating a perfect match between the candidate and reference translations for this particular sentence.
Comparison
| Metric | Description |
|---|---|
| Corpus-Level BLEU | Average BLEU score across all sentences in a corpus. |
| Sentence-Level BLEU | BLEU score for a single sentence pair. |
The choice between corpus-level and sentence-level BLEU depends on the evaluation objective. For overall translation quality assessment, corpus-level BLEU is preferred. For analyzing the quality of individual translations, sentence-level BLEU is more appropriate.
Conclusion
NLTK provides convenient functions for calculating both corpus-level and sentence-level BLEU scores. The choice between these metrics depends on the specific evaluation needs. Understanding the differences between them is crucial for effectively evaluating machine translation systems.