Bleualign
An MT-based sentence alignment tool
Copyright â“’ 2010 Rico Sennrich [email protected]
A project of the Computational Linguistics Group at the University of Zurich (http://www.cl.uzh.ch).
Project Homepage: http://github.com/rsennrich/bleualign
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation
GENERAL INFO
Bleualign is a tool to align parallel texts (i.e. a text and its translation) on a sentence level. Additionally to the source and target text, Bleualign requires an automatic translation of at least one of the texts. The alignment is then performed on the basis of the similarity (modified BLEU score) between the source text sentences (translated into the target language) and the target text sentences. See section PUBLICATIONS for more details.
Obtaining an automatic translation is up to the user. The only requirement is that the translation must correspond line-by-line to the source text (no line breaks inserted or removed).
REQUIREMENTS
The software was developed on Linux using Python 2.6, but should also support newer versions of Python (including 3.X) and other platforms. Please report any issues you encounter to [email protected]
USAGE INSTRUCTIONS
The input and output formats of bleualign are one sentence per line. A line which only contains .EOA is considered a hard delimiter (end of article). Sentence alignment does not cross these delimiters: reliable delimiters improve speed and performance, wrong ones will seriously degrade performance.
Given the files sourcetext.txt, targettext.txt and sourcetranslation.txt (the latter being sentence-aligned with sourcetext.txt), a sample call is
./bleualign.py -s sourcetext.txt -t targettext.txt --srctotarget sourcetranslation.txt -o outputfile
It is also possible to provide several translations and/or translations in the other translation direction. bleualign will run once per translation provided, the final output being the intersection of the individual runs (i.e. sentence pairs produced in each individual run).
./bleualign.py -s sourcetext.txt -t targettext.txt --srctotarget sourcetranslation1.txt --srctotarget sourcetranslation2.txt --targettosrc targettranslation1.txt -o outputfile
./bleualign.py -h will show more usage options
To facilitate batch processing multiple files, batch_align.py can be used.
python batch_align directory source_suffix target_suffix translation_suffix
example: given the directory raw_files with the files 0.de, 0.fr and 0.trans and so on, (0.trans being the translation of 0.de into the target language), then this command will align all files:
python batch_align.py raw_files de fr trans
This will produce the files 0.de.aligned and 0.fr.aligned
Input files are expected to use UTF-8 encoding.
USAGE AS PYTHON MODULE
Bleualign works as stand-alone script, but can also be imported as a module other Python projects. For code examples, see the example/ directory. If you want to know all options, you can see Aligner.default_options variable in bleualign/aligner.py.
To use Bleualign as a Python module, the package needs to be installed (from a local copy) with:
python setup.py install
The Bleualign package can also be installed directly from Github with:
pip install git+https://github.com/rsennrich/Bleualign.git
EVALUATION
Two hand-aligned documents are provided with the repository for development and testing.
Evaluation is performed if you add the argument -d for the development set, and -e for the test set.
An example command for aligning the development set (one long document with 468/554 sentences in DE/FR):
./bleualign.py --source eval/eval1957.de --target eval/eval1957.fr --srctotarget eval/eval1957.europarlfull.fr -d
An example command for aligning the test set (7 documents, totalling 993/1011 sentences in DE/FR):
./bleualign.py --source eval/eval1989.de --target eval/eval1989.fr --srctotarget eval/eval1989.europarlfull.fr -e
PUBLICATIONS
The algorithm is described in
Rico Sennrich, Martin Volk (2010): MT-based Sentence Alignment for OCR-generated Parallel Texts. In: Proceedings of AMTA 2010, Denver, Colorado.
Rico Sennrich; Martin Volk (2011): Iterative, MT-based sentence alignment of parallel texts. In: NODALIDA 2011, Nordic Conference of Computational Linguistics, Riga.
CONTACT
For questions and feeback, please contact [email protected] or use the GitHub repository.