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# Copied from https://github.com/tensorflow/nmt/blob/0be864257a76c151eef20ea689755f08bc1faf4e/nmt/scripts/bleu.py
# 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] + 1.0) / (possible_matches_by_order[i] + 1.0)
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((1.0 / 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 ratio > 1.0:
bp = 1.0
else:
bp = math.exp(1 - 1.0 / ratio)
bleu = geo_mean * bp
return {
"bleu": bleu,
**{f"precision-{i+1}": round(p, 4) for i, p in enumerate(precisions)},
"brevity_penalty": bp,
"length_ratio": ratio,
"translation_length": translation_length,
"reference_length": reference_length,
}
def postprocess_text(text):
# TODO: Tokenize properly
return text.split()
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