NLTK: corpus-level bleu vs sentence-level BLEU score

Question

I have imported nltk in python to calculate BLEU Score on Ubuntu. I understand how sentence-level BLEU score works, but I don\'t understand how corpus-level BLEU score work.

Answer 1

Let's take a look:

>>> help(nltk.translate.bleu_score.corpus_bleu)
Help on function corpus_bleu in module nltk.translate.bleu_score:

corpus_bleu(list_of_references, hypotheses, weights=(0.25, 0.25, 0.25, 0.25), smoothing_function=None)
    Calculate a single corpus-level BLEU score (aka. system-level BLEU) for all 
    the hypotheses and their respective references.  

    Instead of averaging the sentence level BLEU scores (i.e. marco-average 
    precision), the original BLEU metric (Papineni et al. 2002) accounts for 
    the micro-average precision (i.e. summing the numerators and denominators
    for each hypothesis-reference(s) pairs before the division).
    ...

You're in a better position than me to understand the description of the algorithm, so I won't try to "explain" it to you. If the docstring does not clear things up enough, take a look at the source itself. Or find it locally:

>>> nltk.translate.bleu_score.__file__
'.../lib/python3.4/site-packages/nltk/translate/bleu_score.py'