Created
January 27, 2018 19:54
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BLEU version we're using for Babel
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# Copyright 2017 Google Inc. All Rights Reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
# ============================================================================== | |
"""Python implementation of BLEU and smooth-BLEU. | |
This module provides a Python implementation of BLEU and smooth-BLEU. | |
Smooth BLEU is computed following the method outlined in the paper: | |
Chin-Yew Lin, Franz Josef Och. ORANGE: a method for evaluating automatic | |
evaluation metrics for machine translation. COLING 2004. | |
""" | |
import collections | |
import math | |
def _get_ngrams(segment, max_order): | |
"""Extracts all n-grams upto a given maximum order from an input segment. | |
Args: | |
segment: text segment from which n-grams will be extracted. | |
max_order: maximum length in tokens of the n-grams returned by this | |
methods. | |
Returns: | |
The Counter containing all n-grams upto max_order in segment | |
with a count of how many times each n-gram occurred. | |
""" | |
ngram_counts = collections.Counter() | |
for order in range(1, max_order + 1): | |
for i in range(0, len(segment) - order + 1): | |
ngram = tuple(segment[i:i + order]) | |
ngram_counts[ngram] += 1 | |
return ngram_counts | |
def compute_bleu(reference_corpus, translation_corpus, max_order=4, | |
smooth=False): | |
"""Computes BLEU score of translated segments against one or more references. | |
Args: | |
reference_corpus: list of lists of references for each translation. Each | |
reference should be tokenized into a list of tokens. | |
translation_corpus: list of translations to score. Each translation | |
should be tokenized into a list of tokens. | |
max_order: Maximum n-gram order to use when computing BLEU score. | |
smooth: Whether or not to apply Lin et al. 2004 smoothing. | |
Returns: | |
3-Tuple with the BLEU score, n-gram precisions, geometric mean of n-gram | |
precisions and brevity penalty. | |
""" | |
matches_by_order = [0] * max_order | |
possible_matches_by_order = [0] * max_order | |
reference_length = 0 | |
translation_length = 0 | |
for (references, translation) in zip(reference_corpus, | |
translation_corpus): | |
reference_length += min(len(r) for r in references) | |
translation_length += len(translation) | |
merged_ref_ngram_counts = collections.Counter() | |
for reference in references: | |
merged_ref_ngram_counts |= _get_ngrams(reference, max_order) | |
translation_ngram_counts = _get_ngrams(translation, max_order) | |
overlap = translation_ngram_counts & merged_ref_ngram_counts | |
for ngram in overlap: | |
matches_by_order[len(ngram) - 1] += overlap[ngram] | |
for order in range(1, max_order + 1): | |
possible_matches = len(translation) - order + 1 | |
if possible_matches > 0: | |
possible_matches_by_order[order - 1] += possible_matches | |
precisions = [0] * max_order | |
for i in range(0, max_order): | |
if smooth: | |
precisions[i] = ((matches_by_order[i] + 1.) / | |
(possible_matches_by_order[i] + 1.)) | |
else: | |
if possible_matches_by_order[i] > 0: | |
precisions[i] = (float(matches_by_order[i]) / | |
possible_matches_by_order[i]) | |
else: | |
precisions[i] = 0.0 | |
if min(precisions) > 0: | |
p_log_sum = sum((1. / max_order) * math.log(p) for p in precisions) | |
geo_mean = math.exp(p_log_sum) | |
else: | |
geo_mean = 0 | |
ratio = (float(translation_length) / reference_length) if reference_length > 0 else 0.0 | |
if ratio > 1.0: | |
bp = 1. | |
else: | |
bp = math.exp(1 - 1. / ratio) if ratio > 0 else 0.0 | |
bleu = geo_mean * bp | |
return (bleu, precisions, bp, ratio, translation_length, reference_length) |
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