baseline.md

Running a baseline

This document describes how to generate a parallel corpus from a CommonCrawl crawl. It requires that language meta-data has been extracted from the crawl via the process described in metadata\metadata.md and is accessible through a meta-data server. Another requirement is that the software described in install.md has been installed. Baseline in this context means that a simple URL matching mechanism is applied along with standard sentence alignment provided by the Bitextor/hunalign tools.

For the easiest start we also provide already matched URL files annotated with CommonCrawl locations for a selection of language pairs that can be used to start in Step 4. of this baseline process. The files are contained in compressed form in our releases on https://github.com/ModernMT/DataCollection/releases

Step 1: Preparations

Creating a folder for the baseline results

cd
mkdir -p experiments/baseline/en_de_2015_32
cd experiments/baseline/en_de_2015_32

In this case a parallel English-German corpus should be extracted from the 2015_32 (July 2015) CommonCrawl. The crawl identifiers can be found here http://commoncrawl.org/the-data/get-started/.

Step 2: Produce candidate urls

One result of the meta-data processing is a _kv file that contains the CommonCrawl URLs tagged with languages identified for the page content with a language identifier. KV files for some of the CommonCrawls can be found at http://www.statmt.org/~buck/mmt/langstat_kv/

Extracting candidate URLs for the target language

nohup gzip -cd /mnt/langsplit/2015_32_kv.gz | /usr/bin/parallel -j 4 --block=100M --pipe ~/DataCollection/baseline/langstat2candidates.py -lang de > candidates_de 2> candidates_target.log &

The use of GNU Parallel is optional, but speeds things up - here with the use of four processor cores in parallel. It doesn't make sense to run parallel with more jobs than processor cores (-j 4), you can also set -j 0 that uses as many as possible (see GNU Parallel documentation http://www.gnu.org/software/parallel/man.html).

Sorting the target language URL candidates

nohup sort -u -k 1,1 --compress-program=pigz candidates_de > candidates.de 2> candidates_target_sort.log &

Matching the target language URLs with source language URLs

nohup gzip -cd /mnt/langsplit/2015_32_kv.gz | /usr/bin/parallel -j 4 --block=100M --pipe ~/DataCollection/baseline/langstat2candidates.py -lang=en -candidates candidates.de | sort -u -k 1,1 --compress-program=pigz > candidates.en-de 2> match.log &

Particularly if the target language is English, this command might run out of memory. For the typical CommonCrawl size this can be fixed by increasing RAM to 32 GB and omit parallelization:

nohup gzip -cd /mnt/langsplit/2015_32_kv.gz | ~/DataCollection/baseline/langstat2candidates.py -lang=en -candidates candidates.de | sort -u -k 1,1 --compress-program=pigz > candidates.en-de 2> match.log &

If you are collecting data for a language direction for which you already earlier collected data from the reverse direction, please see an optimized process in the appendix.

Step 3: Look up where these URLs appear in CommonCrawl S3

nohup cat candidates.en-de | nice ~/DataCollection/baseline/locate_candidates.py - - -server='http://statmt.org:8084/query_prefix' > candidates.en-de.locations 2> locate.log &

Step 4: Download pages from CommonCrawl S3 and extract text

For certain language pairs we provide the .locations files in compressed form in our releases on https://github.com/ModernMT/DataCollection/releases. You can use these files to start the process in this step.

nohup cat candidates.en-de.locations | ~/DataCollection/baseline/candidates2corpus.py -source_splitter='/moses_install_location/scripts/ems/support/split-sentences.perl -l en -b -q' -target_splitter='/moses_install_location/scripts/ems/support/split-sentences.perl -l de -b -q' > en-de.down 2> candidates2corpus.log &

This step uses the language-specific sentence splitter contained in the Moses MT system. See the installation instructions in INSTALL.md for details.

Step 5: Run Bitextor/Hunalign to extract parallel sentences

nohup cat en-de.down | parallel --pipe /usr/local/bin/bitextor-align-segments --lang1 en --lang2 de -d ~/DataCollection/dicts/en-de.dic > en-de.sent 2> align.log &

(If the machine runs out of memory in this step try pv instead of cat.)

Running the sentence alignment requires a word-based bilingual dictionary in the format required by Bitextor:

• First line: <source language identifier><tab><target language identifier>
• Remaining lines: <source word><tab><target word>

Some dictionaries are available in Bitextor and some in the dicts folder in this repository. See instructions in the appendix how to create a dictionary for a reverse language direction.

The resulting en-de.sent file has 5 columns: source URL, target URL, source text, target text, and hunalign score. The columns can be extracted into individual files using the cut command, e.g. cut -f 3 en-de.sent to extract the source text.

Step 6: Clean parallel sentences

This step is optional, but applies some common-sense cleaning filters to the extracted bitext.

nohup cat en-de.sent | ~/DataCollection/baseline/filter_hunalign_bitext.py - en-de.filtered --lang1 en --lang2 de -cld2 -deleted en-de.deleted 2> filter.log &

This needs cld2-cffi and langid so run pip install langid cld2-cffi first. The resulting file has 3 columns: source text, target text, and hunalign score. As above use cut to get source/target.

Step 7: Extracting the text by web domain

If the extraction by pairs of corpus files by web domain is desired rather than large parallel corpus files, the following commands can be used:

mkdir webdomain_registered
cd webdomain_registered
nohup python ~/DataCollection/baseline/corpus_by_domain.py -slang en -tlang de --regdomain ../en-de.filtered 2> ../webdomain_registered_err.log &

The option --regdomain extracts the files by registered domain (i.e. without subdomains). The parameter can be omitted to extract by subdomain.

Appendix

Generating candidate URL pairs for reverse language directions (optional optimization)

Because of the way that URL candidate extraction works, the extraction for a reverse language direction - in our example German-Italian - would generate a lot of duplicate candidates (in some of our experiments around 90%). Because we do not have a way to detect the translation direction (i.e. what was the original text and what the translation), this generates a lot of duplicate work. Thus the corpus from the original language direction (in our case English-German) can just be reversed. In order to collect data only for pages that were not contained in the original language direction, this process can be used:

awk '{print $1 " " $4 " " $3 " " $2 " " $5}' /location_original_language_direction/candidates.en-de > candidates.de-en.exclude
sort candidates.de-en candidates.de-en.exclude candidates.de-en.exclude | uniq -u > candidates.de-en.unique

Then use the file candidates.de-en.unique as input for Step 3.

Creating dictionaries for reverse language directions

To create a dictionary for a reverse language from a dictionary in Bitextor format (described in Step 5):

• Remove first line with language identifiers and save the result as en-de.nohead.dic
• Reverse the order of the dictionary entries and sort the results with the command awk '{print $2 " " $1}' en-de.nohead.dic | sort > de-en.dic
• Add a header line de<tab>en to the reversed de-en.dic