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bitextor / bifixer

Tool to fix bitexts and tag near-duplicates for removal
GNU General Public License v3.0
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bifixer

Tool to fix bitexts and tag near-duplicates for removal.

License

What can BIFIXER do to your parallel corpora

Citation

If you find Bifixer useful, please consider citing the following paper:

Gema Ramírez-Sánchez, Jaume Zaragoza-Bernabeu, Marta Bañón and Sergio Ortiz Rojas \ "Bifixer and Bicleaner: two open-source tools to clean your parallel data.",\ in Proceedings of the 22nd Annual Conference of the European Association for Machine Translation.\ Lisboa, Portugal: European Association for Machine Translation, November 2020

@InProceedings{prompsit:2020:EAMT,
  author    = {Gema Ram\'{i}rez-S\'{a}nchez and Jaume Zaragoza-Bernabeu and Marta Ba{\~n}\'{o}n and Sergio Ortiz-Rojas},
  title     = {Bifixer and Bicleaner: two open-source tools to clean your parallel data.},
  booktitle = {Proceedings of the 22nd Annual Conference of the European Association for Machine Translation},
  pages     = {291--298},
  isbn      = {978-989-33-0589-8},
  year      = {2020},
  month     = {November},
  address   = {Lisboa, Portugal},
  publisher = {European Association for Machine Translation}
}

INSTALLATION

Install from source:

git clone https://github.com/bitextor/bifixer
cd bifixer
pip install .

Automatic testing was added to ensure that everything is working fine in Bifixer:

cd bifixer
pytest

Or install without manually downloading the repo:

pip install "bifixer @ git+https://github.com/bitextor/bifixer.git"

Or even easier, install directly from PyPI:

pip install bifixer

Also, you can install the conda package:

conda install -c bitextor bifixer

After installing, two executables (bifixer and monofixer) will be available to be run.

Loomchild segmenter

Please note that, in order to use the optional loomchild segmenter module in Java, it has to be specified as an optional dependency during installation:

pip install bifixer[loomchild]

In case you are not using Java 8 as default, download it and overwrite the 'JAVA_HOME' variable before installing, for example:

export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/

USAGE

Bifixer

usage: bifixer.py [-h] [--header] [--scol SCOL] [--tcol TCOL]
                  [--sdeferredcol SDEFERREDCOL] [--tdeferredcol TDEFERREDCOL]
                  [--ignore_characters] [--ignore_empty] [--ignore_long]
                  [--ignore_orthography] [--ignore_detokenization]
                  [--ignore_duplicates] [--aggressive_dedup]
                  [--ignore_segmentation] [--ignore_html]
                  [--words_before_segmenting WORDS_BEFORE_SEGMENTING]
                  [--segmenter {nltk,loomchild}] [--annotated_output] [--tmp_dir TMP_DIR] [-q]
                  [--debug] [--logfile LOGFILE] [-v]
                  input output srclang trglang

positional arguments:
  input                 Tab-separated files to be bifixed
  output                Fixed corpus
  srclang               Source language (SL) of the input
  trglang               Target language (TL) of the input

optional arguments:
  -h, --help            show this help message and exit

Optional:
  --header              Input file will have header (default: False)
  --scol SCOL           Source sentence column (starting in 1). The name of
                        the field is expected instead of the position if
                        --header is set (default: 3)
  --tcol TCOL           Target sentence column (starting in 1). The name of
                        the field is expected instead of the position if
                        --header is set (default: 4)
  --sdeferredcol SDEFERREDCOL
                        Source deferred standoff annotation column (starting
                        in 1). The name of the field is expected instead of
                        the position if --header is set (default: None)
  --tdeferredcol TDEFERREDCOL
                        Target deferred standoff annotation column (starting
                        in 1). The name of the field is expected instead of
                        the position if --header is set (default: None)
  --ignore_characters   Doesn't fix mojibake, orthography, or other character
                        issues (default: False)
  --ignore_empty        Doesn't remove sentences with empty source or target
                        (default: False)
  --ignore_long         Doesn't ignore too long sentences (default: False)
  --ignore_orthography  Doesn't apply orthography fixing (default: False)
  --ignore_html     Doesn't remove HTML tags (default: False)
  --ignore_detokenization
                        Doesn't fix common tokenization issues (default:
                        False)
  --ignore_duplicates   Doesn't obtain the hashes of parallel sentences
                        (default: False)
  --aggressive_dedup    Treats similar sentences as duplicates (marking them
                        with the same hash) (default: False)
  --ignore_segmentation
                        Doesn't change segmentation of long sentences
                        (default: False)
  --words_before_segmenting WORDS_BEFORE_SEGMENTING
                        Max words allowed in one side of a parallel sentence
                        before trying to segmentate it. Set to 0 to applicate
                        segmentation on everything. (default: 15)
  --segmenter {nltk,loomchild}
                        Segmenter module. (default: nltk)
  --annotated_output    Adds an extra column indicating if the sentence pair was modified
             ('Yes' if it was modified, otherwise 'No') (default: False)

  --tmp_dir TMP_DIR     Temporary directory where creating the temporary files
                        of this program (default: /tmp)

Logging:
  -q, --quiet           Silent logging mode (default: False)
  --debug               Debug logging mode (default: False)
  --logfile LOGFILE     Store log to a file (default: <_io.TextIOWrapper
                        name='<stderr>' mode='w' encoding='UTF-8'>)
  -v, --version         show version of this script and exit

Parameters

Monofixer

python3.7 bifixer/monofixer.py --help
usage: monofixer.py [-h] 
                    [--scol SCOL] [--sdeferredcol SDEFERREDCOL]
                    [--ignore_characters] [--ignore_long]
                    [--ignore_orthography] [--ignore_detokenization]
                    [--ignore_duplicates] [--aggressive_dedup]
                    [--ignore_segmentation] [--ignore_html]
                    [--words_before_segmenting WORDS_BEFORE_SEGMENTING]
                    [--segmenter {nltk,loomchild}] [--annotated_output] [--tmp_dir TMP_DIR] [-q]
                    [--debug] [--logfile LOGFILE] [-v]
                    input output lang

positional arguments:
  input                 Tab-separated file to be fixed
  output                Fixed corpus
  lang                  Language of the input

optional arguments:
  -h, --help            show this help message and exit

Optional:
  --header              Input file will have header (default: False)
  --scol SCOL           Sentence column (starting in 1). The name of the
                        field is expected instead of the position if --header
                        is set (default: 2)
  --sdeferredcol SDEFERREDCOL
                        Source deferred standoff annotation column (starting
                        in 1). The name of the field is expected instead of
                        the position if --header is set (default: None)
  --ignore_characters   Doesn't fix mojibake, orthography, or other character
                        issues (default: False)
  --ignore_long         Doesn't ignore too long sentences (default: False)
  --ignore_orthography  Doesn't apply orthography fixing (default: False)
  --ignore_detokenization
                        Doesn't fix common tokenization issues (default:
                        False)
  --ignore_html     Doesn't remove HTML tags (default: False)
  --ignore_duplicates   Doesn't obtain the hashes of sentences (default:
                        False)
  --aggressive_dedup    Treats similar sentences as duplicates (marking them
                        with the same hash) (default: False)
  --ignore_segmentation 
                        Doesn't change segmentation of long sentences
                        (default: False)
  --words_before_segmenting WORDS_BEFORE_SEGMENTING
                        Max words allowed in a parallel sentence before trying
                        to segmentate it. Set to 0 to applicate segmentation
                        on everyt33hing. (default: 15)
  --segmenter {nltk,loomchild}
                        Segmenter module. (default: nltk)
  --annotated_output    Adds an extra column indicating if the sentence  was
             modified ('Yes' if it was modified, otherwise 'No')
             (default: False)
  --tmp_dir TMP_DIR     Temporary directory where creating the temporary files
                        of this program (default: /tmp)

Logging:
  -q, --quiet           Silent logging mode (default: False)
  --debug               Debug logging mode (default: False)
  --logfile LOGFILE     Store log to a file (default: <_io.TextIOWrapper
                        name='<stderr>' mode='w' encoding='UTF-8'>)
  -v, --version         show version of this script and exit

Parameters

RUN

Single thread run

bifixer input-corpus.en-es output-corpus.en-es en es

Running in parallel

bifixer can be parallelized by using your favourite method (for example, GNU parallel)

Suggested usage:

cat input-corpus.en-es \
    | parallel -j 25 --pipe -k -l 30000 bifixer -q - - en es \
    > output-corpus.en-es

where the two '-' mean read from stdin and write to stdout, and the -q tells bifixer to be quiet in order to avoid logging a lot of information messages.

TAGGING DUPLICATED AND NEAR-DUPLICATED SENTENCES

In order to ease the later removal of duplicated or near-duplicated parallel sentences, Bifixer appends each parallel sentence two new fields: hashand ranking.

The hash is obtained by using the XXHash algorithm, applied after fixing source and target sentences (fixed_source+"\t"+fixed_target). Sentences that are identical at this step (see example below) will get the same hash.

When using the --aggressive_dedup feature, fixed parallel sentences are also normalized (ignoring casing, accents and diacritics) before their hash is computed. Doing so, sentences that are near-duplicates (i.e. they only differ in casing or accents) will also get the same hash. Normalization is only used internally: the output sentences will not be normalized after Bifixer is applied.

A ranking column is added at the end of each line. When not using the --aggressive_dedup feature, the number is set to 1 by default. When using the --aggressive_dedup feature, a float number is provided. This number (interpreted as the higher the better) will be used at later step to help the deduplication algorithm to choose the best sentence from those sharing the same hash. If the ranking number is exactly the same for a group of sentences sharing the same hash, only a random one should be kept. Otherwise, the one with the highest ranking number should be kept.

EXAMPLE

Input file:

http://www.ehyz.com/2.html.tmp  http://www.ehyz.com/2.html.tmp  1 year ago NuVid    Hace 1 año NuVid
http://pandafoundation.com/index.php?page=7 http://pandafoundation.com/index.php?page=26    ©2007 Chengdu Research Base of Giant Panda Breeding ! All Rights Reserved   ©2017 Fundación para la Investigación de Cría del Panda Gigante de Chengdu/ ¡Todos los derechos reservados!     
http://www.boliviamall.com/4520.html    http://www.boliviamall.com/4520.html    Welcome Guest 1! Would you like to log in ? Bienvenido Invitado 1! ¿Le gustaria entrar ?    
http://pandafoundation.com/index.php?page=157   http://pandafoundation.com/index.php?page=76    ©2007 Chengdu Research Base of Giant Panda Breeding ! All Rights Reserved   ©2017 Fundación para la Investigación de Cría del Panda Gigante de Chengdu/ ¡Todos los derechos reservados!
http://www.ehyz.com/6.html.tmp  http://www.ehyz.com/6.html.tmp  1 year ago NuVid    Hace 1 año NuVid
http://www.boliviamall.com/4305.html    http://www.boliviamall.com/4305.html    Welcome Guest 12! Would you like to log in? ¡Bienvenido invitado 12! ¿Le gustaria entrar?

Output file (using the '--aggressive_dedup' feature, otherwise ranking number would be 1 in all cases):


http://www.ehyz.com/2.html.tmp  http://www.ehyz.com/2.html.tmp  1 year ago NuVid    Hace 1 año NuVid    9f1f7c6fc775a23a    88.25
http://pandafoundation.com/index.php?page=7 http://pandafoundation.com/index.php?page=26    ©2007 Chengdu Research Base of Giant Panda Breeding ! All Rights Reserved   ©2017 Fundación para la Investigación de Cría del Panda Gigante de Chengdu/ ¡Todos los derechos reservados! d0278d1279f06823    91.93
http://www.boliviamall.com/4520.html    http://www.boliviamall.com/4520.html    Welcome Guest 1! Would you like to log in ? Bienvenido Invitado 1! ¿Le gustaría entrar ?    e8f129b1624b9f5d    91.22
http://pandafoundation.com/index.php?page=157   http://pandafoundation.com/index.php?page=76    ©2007 Chengdu Research Base of Giant Panda Breeding ! All Rights Reserved   ©2017 Fundación para la Investigación de Cría del Panda Gigante de Chengdu/ ¡Todos los derechos reservados! d0278d1279f06823    91.93
http://www.ehyz.com/6.html.tmp  http://www.ehyz.com/6.html.tmp  1 year ago NuVid    Hace 1 año NuVid    9f1f7c6fc775a23a    88.25
http://www.boliviamall.com/4305.html    http://www.boliviamall.com/4305.html    Welcome Guest 12! Would you like to log in? ¡Bienvenido invitado 12! ¿Le gustaría entrar?   422aeefd8f056b30    92.78

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