Parallel corpora cleaning and evaluation tool
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aligner
classifier
Readme.md
classifier.jar
cleaner.py
features.py
pre-features.py
shuffle.py

Readme.md

C-Eval - paralllel corpora cleaning and evaluation tool

C-Eval presents a method for cleaning and evaluating parallel corpora using word alignments and machine learning algorithms. It is based on the assumption that parallel sentences have many word alignments while non-parallel sentences have few or none. We show that it is possible to build an automatic classifier, which identifies most of non-parallel sentences in a parallel corpus. This method allows us to do (1) automatic quality evaluation of parallel corpus and (2) automatic parallel corpus cleaning. The method allows us to get cleaner parallel corpora, smaller statistical models, and faster MT training, but this does not always guarantee higher BLEU scores.

Requirements

We use fast align for word alignment and Weka for machine learning.

Training

First, you need to train a model on a good parallel corpus.

Run:

pypy cleaner.py                   \
  -train                          \
  -s corpus.en -t corpus.fr       \
  -a fastalign -c reptree         \
  -m corpus.en-fr.fastalign.reptree.model

Arguments:

  • -train - perform training
  • -s <source corpus> - source corpus
  • -t <target corpus> - target corpus
  • -a <aligner> - word aligner, always fastalign
  • -c <classifier> - classifier (machine learning algorithm): extratrees, j48 or reptree
  • -m <model filename> - output filename for the trained model

Cleaning

Run:

pypy cleaner.py                                \
    -s othercorpus.en -t othercorpus.fr        \  
    -a fastalign -m corpus.en-fr.fastalign.reptree.model

Arguments:

  • -s <source corpus> - source corpus
  • -t <target corpus> - target corpus
  • -a <aligner> - word aligner, always fastalign
  • -m <model filename> previously trained model

The script has to be run from the corpus directory. The output files will be produced in the corpus folder named as <source corpus>.filtered and <target corpus>.filtered, e.g.,othercorpus.en.filtered.txt, and othercorpus.en.filtered.BAD.txt