compute_bleu.py

# Copyright 2018 The TensorFlow Authors. 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.
# ==============================================================================
"""Script to compute official BLEU score.

Source:
https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/bleu_hook.py
"""

import re
import sys
import unicodedata

from absl import app
from absl import flags
import six
from six.moves import range
import tensorflow as tf

from official.nlp.transformer.utils import metrics
from official.nlp.transformer.utils import tokenizer
from official.utils.flags import core as flags_core


class UnicodeRegex(object):
  """Ad-hoc hack to recognize all punctuation and symbols."""

  def __init__(self):
    punctuation = self.property_chars("P")
    self.nondigit_punct_re = re.compile(r"([^\d])([" + punctuation + r"])")
    self.punct_nondigit_re = re.compile(r"([" + punctuation + r"])([^\d])")
    self.symbol_re = re.compile("([" + self.property_chars("S") + "])")

  def property_chars(self, prefix):
    return "".join(
        six.unichr(x)
        for x in range(sys.maxunicode)
        if unicodedata.category(six.unichr(x)).startswith(prefix))


uregex = UnicodeRegex()


def bleu_tokenize(string):
  r"""Tokenize a string following the official BLEU implementation.

  See https://github.com/moses-smt/mosesdecoder/'
           'blob/master/scripts/generic/mteval-v14.pl#L954-L983
  In our case, the input string is expected to be just one line
  and no HTML entities de-escaping is needed.
  So we just tokenize on punctuation and symbols,
  except when a punctuation is preceded and followed by a digit
  (e.g. a comma/dot as a thousand/decimal separator).

  Note that a numer (e.g. a year) followed by a dot at the end of sentence
  is NOT tokenized,
  i.e. the dot stays with the number because `s/(\p{P})(\P{N})/ $1 $2/g`
  does not match this case (unless we add a space after each sentence).
  However, this error is already in the original mteval-v14.pl
  and we want to be consistent with it.

  Args:
    string: the input string

  Returns:
    a list of tokens
  """
  string = uregex.nondigit_punct_re.sub(r"\1 \2 ", string)
  string = uregex.punct_nondigit_re.sub(r" \1 \2", string)
  string = uregex.symbol_re.sub(r" \1 ", string)
  return string.split()


def bleu_wrapper(ref_filename, hyp_filename, case_sensitive=False):
  """Compute BLEU for two files (reference and hypothesis translation)."""
  ref_lines = tokenizer.native_to_unicode(
      tf.io.gfile.GFile(ref_filename).read()).strip().splitlines()
  hyp_lines = tokenizer.native_to_unicode(
      tf.io.gfile.GFile(hyp_filename).read()).strip().splitlines()
  return bleu_on_list(ref_lines, hyp_lines, case_sensitive)


def bleu_on_list(ref_lines, hyp_lines, case_sensitive=False):
  """Compute BLEU for two list of strings (reference and hypothesis)."""
  if len(ref_lines) != len(hyp_lines):
    raise ValueError(
        "Reference and translation files have different number of "
        "lines (%d VS %d). If training only a few steps (100-200), the "
        "translation may be empty." % (len(ref_lines), len(hyp_lines)))
  if not case_sensitive:
    ref_lines = [x.lower() for x in ref_lines]
    hyp_lines = [x.lower() for x in hyp_lines]
  ref_tokens = [bleu_tokenize(x) for x in ref_lines]
  hyp_tokens = [bleu_tokenize(x) for x in hyp_lines]
  return metrics.compute_bleu(ref_tokens, hyp_tokens) * 100


def main(unused_argv):
  if FLAGS.bleu_variant in ("both", "uncased"):
    score = bleu_wrapper(FLAGS.reference, FLAGS.translation, False)
    tf.logging.info("Case-insensitive results: %f" % score)

  if FLAGS.bleu_variant in ("both", "cased"):
    score = bleu_wrapper(FLAGS.reference, FLAGS.translation, True)
    tf.logging.info("Case-sensitive results: %f" % score)


def define_compute_bleu_flags():
  """Add flags for computing BLEU score."""
  flags.DEFINE_string(
      name="translation",
      default=None,
      help=flags_core.help_wrap("File containing translated text."))
  flags.mark_flag_as_required("translation")

  flags.DEFINE_string(
      name="reference",
      default=None,
      help=flags_core.help_wrap("File containing reference translation."))
  flags.mark_flag_as_required("reference")

  flags.DEFINE_enum(
      name="bleu_variant",
      short_name="bv",
      default="both",
      enum_values=["both", "uncased", "cased"],
      case_sensitive=False,
      help=flags_core.help_wrap(
          "Specify one or more BLEU variants to calculate. Variants: \"cased\""
          ", \"uncased\", or \"both\"."))


if __name__ == "__main__":
  tf.logging.set_verbosity(tf.logging.INFO)
  define_compute_bleu_flags()
  FLAGS = flags.FLAGS
  app.run(main)