：
>>> import nltk
>>> hypothesis = ['This', 'is', 'cat']
>>> reference = ['This', 'is', 'a', 'cat']
>>> references = [reference] # list of references for 1 sentence.
>>> list_of_references = [references] # list of references for all sentences in corpus.
>>> list_of_hypotheses = [hypothesis] # list of hypotheses that corresponds to list of references.
>>> nltk.translate.bleu_score.corpus_bleu(list_of_references, list_of_hypotheses)
0.6025286104785453
>>> nltk.translate.bleu_score.sentence_bleu(references, hypothesis)
0.6025286104785453
（注意：您必须在develop分支上提取最新版本的NLTK才能获得BLEU评分实施的稳定版本）
：
其实只要有一个参考，在你的整个语料库一个假设，既corpus_bleu()与sentence_bleu()应返回相同的值，如上面的例子。
在代码中，我们看到sentence_bleu实际上是鸭子类型corpus_bleu：
def sentence_bleu(references, hypothesis, weights=(0.25, 0.25, 0.25, 0.25),
smoothing_function=None):
return corpus_bleu([references], [hypothesis], weights, smoothing_function)
如果我们查看以下参数sentence_bleu：
def sentence_bleu(references, hypothesis, weights=(0.25, 0.25, 0.25, 0.25),
smoothing_function=None):
""""
:param references: reference sentences
:type references: list(list(str))
:param hypothesis: a hypothesis sentence
:type hypothesis: list(str)
:param weights: weights for unigrams, bigrams, trigrams and so on
:type weights: list(float)
:return: The sentence-level BLEU score.
:rtype: float
"""
sentence_bleu的引用输入为list(list(str))。
因此，如果您有一个句子字符串，例如"This is a cat"，则必须对其进行标记以获取字符串列表，["This", "is", "a", "cat"]并且由于它允许多个引用，因此它必须是字符串列表的列表，例如，如果您有第二个引用，“这是一条猫”，您的输入sentence_bleu()将是：
references = [ ["This", "is", "a", "cat"], ["This", "is", "a", "feline"] ]
hypothesis = ["This", "is", "cat"]
sentence_bleu(references, hypothesis)
当涉及到corpus_bleu()list_of_references参数时，它基本上是一个列表，该列表包含了所有sentence_bleu()作为引用的内容：
def corpus_bleu(list_of_references, hypotheses, weights=(0.25, 0.25, 0.25, 0.25),
smoothing_function=None):
"""
:param references: a corpus of lists of reference sentences, w.r.t. hypotheses
:type references: list(list(list(str)))
:param hypotheses: a list of hypothesis sentences
:type hypotheses: list(list(str))
:param weights: weights for unigrams, bigrams, trigrams and so on
:type weights: list(float)
:return: The corpus-level BLEU score.
:rtype: float
"""
除了查看内的doctest之外nltk/translate/bleu_score.py，您还可以查看unittest，nltk/test/unit/translate/test_bleu_score.py以了解如何使用内的每个组件bleu_score.py。
顺便说一句，由于使用（（）（https://github.com/nltk/nltk/blob/develop/nltk/translate/ .py＃L21）中的sentence_bleu导入，bleu``nltk.translate.__init__.py ****
from nltk.translate import bleu
将与以下相同：
from nltk.translate.bleu_score import sentence_bleu
并在代码中：
>>> from nltk.translate import bleu
>>> from nltk.translate.bleu_score import sentence_bleu
>>> from nltk.translate.bleu_score import corpus_bleu
>>> bleu == sentence_bleu
True
>>> bleu == corpus_bleu
False
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