Long Le Minh Long Le Minh - 4 months ago 95
Python Question

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

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.

Below is my code for corpus-level BLEU score:

import nltk

hypothesis = ['This', 'is', 'cat']
reference = ['This', 'is', 'a', 'cat']
BLEUscore = nltk.translate.bleu_score.corpus_bleu([reference], [hypothesis], weights = [1])

For some reason, the bleu score is 0 for the above code. I was expecting a corpus-level BLEU score of at least 0.5.

Here is my code for sentence-level BLEU score

import nltk

hypothesis = ['This', 'is', 'cat']
reference = ['This', 'is', 'a', 'cat']
BLEUscore = nltk.translate.bleu_score.sentence_bleu([reference], hypothesis, weights = [1])

Here the sentence-level BLEU score is 0.71 which I expect, taking into account the brevity-penalty and the missing word "a". However, I don't understand how corpus-level BLEU score work.

Any help would be appreciated.


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__