从零开始在 python 中的 Bleu 分数

Question

看了吴恩达关于Bleu score 的视频后，我想用python 从头开始​​实现一个。我用 numpy 谨慎地用 python 编写了完整的代码。这是完整的代码

import numpy as np

def n_gram_generator(sentence,n= 2,n_gram= False):
    '''
    N-Gram generator with parameters sentence
    n is for number of n_grams
    The n_gram parameter removes repeating n_grams 
    '''
    sentence = sentence.lower() # converting to lower case
    sent_arr = np.array(sentence.split()) # split to string arrays
    length = len(sent_arr)

    word_list = []
    for i in range(length+1):
        if i < n:
            continue
        word_range = list(range(i-n,i))
        s_list = sent_arr[word_range]
        string = ' '.join(s_list) # converting list to strings
        word_list.append(string) # append to word_list
        if n_gram:
            word_list = list(set(word_list))
    return word_list

def bleu_score(original,machine_translated):
    '''
    Bleu score function given a orginal and a machine translated sentences
    '''
    mt_length = len(machine_translated.split())
    o_length = len(original.split())

    # Brevity Penalty 
    if mt_length>o_length:
        BP=1
    else:
        penality=1-(mt_length/o_length)
        BP=np.exp(penality)

    # calculating precision
    precision_score = []
    for i in range(mt_length):
        original_n_gram = n_gram_generator(original,i)
        machine_n_gram = n_gram_generator(machine_translated,i)
        n_gram_list = list(set(machine_n_gram)) # removes repeating strings

        # counting number of occurence 
        machine_score = 0
        original_score = 0
        for j in n_gram_list:
            machine_count = machine_n_gram.count(j)
            original_count = original_n_gram.count(j)
            machine_score = machine_score+machine_count
            original_score = original_score+original_count

        precision = original_score/machine_score
        precision_score.append(precision)
    precisions_sum = np.array(precision_score).sum()
    avg_precisions_sum=precisions_sum/mt_length
    bleu=BP*np.exp(avg_precisions_sum)
    return bleu

if __name__ == "__main__":
    original = "this is a test"
    bs=bleu_score(original,original)
    print("Bleu Score Original",bs)

我尝试用 nltk's 测试我的分数

from nltk.translate.bleu_score import sentence_bleu
reference = [['this', 'is', 'a', 'test']]
candidate = ['this', 'is', 'a', 'test']
score = sentence_bleu(reference, candidate)
print(score)

问题是我的 bleu 分数大约是 2.718281 而 nltk 的分数是 1。我究竟做错了什么？

以下是一些可能的原因：

1) 我根据机器翻译句子的长度计算了 ngrams。这里从 1 到 4

2) n_gram_generator 自己编写的函数，不确定其准确性

3) 我使用了错误的函数或计算错误的 bleu 分数的一些方法

有人可以查看我的代码并告诉我哪里出错了吗？

Answer 1

这里是修改后的解决方案

# coding: utf-8

import numpy as np
from collections import Counter
import math
from nltk.translate.bleu_score import sentence_bleu


def n_gram_generator(sentence,n= 2,n_gram= False):
    '''
    N-Gram generator with parameters sentence
    n is for number of n_grams
    The n_gram parameter removes repeating n_grams
    '''
    sentence = sentence.lower()  # converting to lower case
    sent_arr = np.array(sentence.split())  # split to string arrays
    length = len(sent_arr)

    word_list = []
    for i in range(length+1):
        if i < n:
            continue
        word_range = list(range(i-n,i))
        s_list = sent_arr[word_range]
        string = ' '.join(s_list)  # converting list to strings
        word_list.append(string) # append to word_list
        if n_gram:
            word_list = list(set(word_list))
    return word_list


def bleu_score(original, machine_translated):
    '''
    Bleu score function given a orginal and a machine translated sentences
    '''
    mt_length = len(machine_translated.split())
    o_length  = len(original.split())

    # Brevity Penalty
    if mt_length > o_length:
        BP=1
    else:
        penality=1-(mt_length/o_length)
        BP = np.exp(penality)

    # Clipped precision
    clipped_precision_score = []
    for ngram_level in range(1, 5):  # 1-gram to 4-gram
        
        
        original_ngram_list = n_gram_generator(original, ngram_level)
        original_n_gram = Counter(original_ngram_list)
        
        machine_ngram_list = n_gram_generator(machine_translated, ngram_level)
        machine_n_gram = Counter(machine_ngram_list)
        
        
        num_ngrams_in_translation = sum(machine_n_gram.values())  # number of ngrams in translation
        
        # iterate the unique ngrams in translation (candidate)
        for j in machine_n_gram:
            
            if j in original_n_gram:  # if found in reference
                
                if machine_n_gram[j] > original_n_gram[j]:  # CLIPPING - if found in translation more than in source, clip
                    machine_n_gram[j] = original_n_gram[j]
                    
            else:
                machine_n_gram[j] = 0

        #print (sum(machine_n_gram.values()), c)
        clipped_precision_score.append(float(sum(machine_n_gram.values())) / num_ngrams_in_translation)

    #print (clipped_precision_score)

    weights = [0.25]*4

    s = (w_i * math.log(p_i) for w_i, p_i in zip(weights, clipped_precision_score))
    s = BP * math.exp(math.fsum(s))
    return s

original = "It is a guide to action which ensures that the military alwasy obeys the command of the party"
machine_translated = "It is the guiding principle which guarantees the military forces alwasy being under the command of the party"

print (bleu_score(original, machine_translated))
print (sentence_bleu([original.split()], machine_translated.split()))

Answer 2

这里是实际nltksource code的略微修改版本：

def sentence_bleu_man(
    references,
    hypothesis,
    weights=(0.25, 0.25, 0.25, 0.25)):

    # compute modified precision for 1-4 ngrams
    p_numerators = Counter()  
    p_denominators = Counter()  
    hyp_lengths, ref_lengths = 0, 0

    for i, _ in enumerate(weights, start=1):
        p_i = modified_precision(references, hypothesis, i)
        p_numerators[i] += p_i.numerator
        p_denominators[i] += p_i.denominator

    # compute brevity penalty    
    hyp_len = len(hypothesis)
    ref_len = closest_ref_length(references, hyp_len)
    bp = brevity_penalty(ref_len, hyp_len)

    # compute final score
    p_n = [
        Fraction(p_numerators[i], p_denominators[i], 
        _normalize=False)
        for i, _ in enumerate(weights, start=1)
        if p_numerators[i] > 0
    ]
    s = (w_i * math.log(p_i) for w_i, p_i in zip(weights, p_n))
    s = bp * math.exp(math.fsum(s))

    return s

我们可以使用原始paper中的一个例子：

rt_raw = [
'It is a guide to action that ensures that the military will forever heed Party commands',
'It is the guiding principle which guarantees the military forces always being under the command of the Party',
'It is the practical guide for the army always to heed the directions of the party'
]

ct_raw = [
'It is a guide to action which ensures that the military always obeys the commands of the party',
'It is to insure the troops forever hearing the activity guidebook that party direct'
]

def process_trans(t):
    return t.lower().split()

rt = [process_trans(t) for t in rt_raw]
ct = [process_trans(t) for t in ct_raw]

c1, c2 = ct[0], ct[1]

sentence_bleu_man(rt, c2, weights=(.5, .5, 0, 0))
sentence_bleu(rt, c2, weights=(.5, .5, 0, 0))

输出：

0.18174699151949172
0.18174699151949172

Answer 3

您的 bleu 分数计算错误。 问题：

• 你必须使用裁剪精度
• sklearn 对每个 n 克使用权重
• sklearn 对 n = 1,2,3,4 使用 ngram

更正的代码

def bleu_score(original,machine_translated):
    '''
    Bleu score function given a orginal and a machine translated sentences
    '''
    mt_length = len(machine_translated.split())
    o_length = len(original.split())

    # Brevity Penalty 
    if mt_length>o_length:
        BP=1
    else:
        penality=1-(mt_length/o_length)
        BP=np.exp(penality)

    # Clipped precision
    clipped_precision_score = []
    for i in range(1, 5):
        original_n_gram = Counter(n_gram_generator(original,i))
        machine_n_gram = Counter(n_gram_generator(machine_translated,i))

        c = sum(machine_n_gram.values())
        for j in machine_n_gram:
            if j in original_n_gram:
                if machine_n_gram[j] > original_n_gram[j]:
                    machine_n_gram[j] = original_n_gram[j]
            else:
                machine_n_gram[j] = 0

        #print (sum(machine_n_gram.values()), c)
        clipped_precision_score.append(sum(machine_n_gram.values())/c)

    #print (clipped_precision_score)

    weights =[0.25]*4

    s = (w_i * math.log(p_i) for w_i, p_i in zip(weights, clipped_precision_score))
    s = BP * math.exp(math.fsum(s))
    return s

original = "It is a guide to action which ensures that the military alwasy obeys the command of the party"
machine_translated = "It is the guiding principle which guarantees the military forces alwasy being under the command of the party"

print (bleu_score(original, machine_translated))
print (sentence_bleu([original.split()], machine_translated.split()))

输出：

0.27098211583470044
0.27098211583470044