Using weights for ambiguous rules

The Idea

The idea is to allow old apertium transfer rules to be ambiguous i.e. allow a set of rules to match the same general input pattern. This is more effective than the existing situation wherein the first rule in the XML transfer files takes exclusive precedence and blocks out all its ambiguous peers during the transfer precompilation stage, often leading to inaccurate translation. To achieve this, the transfer module would use a set of predefined or pre-trained (more specific) weighted patterns provided for each group of ambiguous rules. This way, if a specific pattern matches with multiple transfer rules, the rule with the largest weight for that pattern is applied.

If no weighted patterns are matched, then the first rule in XML transfer file that matches the general pattern is still considered the default one and is applied.

The module (apertium-ambiguous) can be found at https://github.com/sevilaybayatli/apertium-ambiguous.

Configure, build and install

cd to apertium-ambiguous before you run the commands is shown below

./autogen.sh
./configure
make

How to use apertium-ambiguous for your language pair

For this tutorial, we will be using the language pair apertium-kaz-tur

Download a wikimedia dump

Download a Wikipedia dump from http://dumps.wikimedia.org:

$ wget https://dumps.wikimedia.org/kkwiki/latest/kkwiki-latest-pages-articles.xml.bz2

To use any other language, simply replace the occurrences of 'kk' with the 2-letter code of your language.

Next, extract the text using WikiExtractor script:

$ git clone https://github.com/apertium/WikiExtractor.git

$ cd WikiExtractor

$ python3 WikiExtractor.py --infn ../kkwiki-latest-pages-articles.xml.bz2  

The extracted file will be named as wiki.txt in the current directory which you are already working on and you are going to use it with other steps of the project.

Install segmenter

Install Kazakh segmenter from https://github.com/diasks2/pragmatic_segmenter/tree/kazakh

For using pragmatic_segmenter you need to do the following steps:

1. Download Ruby 2.3 by running sudo apt-get install ruby-full
2. Run gem install pragmatic_segmenter

This piece of code uses the segmenter to segment a corpus file and output the segmented sentences into a file. The sentenceTokenizer.rb, which is located at https://github.com/sevilaybayatli/apertium-ambiguous/blob/master/scripts

require 'pragmatic_segmenter'

File.open(ARGV[1]).each do |line1|
	line1.delete! ('\\\(\)\[\]\{\}\<\>\|\$\/\'\"')
    ps = PragmaticSegmenter::Segmenter.new(text: line1, language: ARGV[0], doc_type: 'txt')
    sentences = ps.segment
    
    File.open(ARGV[2], "a") do |line2|
        sentences.each { |sentence| line2.puts sentence }
    end
end

Breaking corpus into sentences using the ruby program sentenceTokenizer.rb built on the pragmatic segmenter.

ruby2.3 sentenceTokenizer.rb $langCode $inputFile sentences.txt

For example:
ruby2.3 sentenceTokenizer.rb kk wiki.txt sentences.txt

langcode for Kazakh kk, inputFile is Kazakh corpus, and sentences.txt is a segmented sentences.

Apertium language pairs modules

You need apertium and the language pair installed for using language modules. The steps below just show how the apertium modules for getting desired output which will used by apertium-ambiguous. Apertium pair parent directory path(apertium-kaz-tur). If it's in your home directory then we expect $HOME.

To apply the apertium tool biltrans on the segmented sentences:

apertium -d $pairPar/apertium-$pairCode $pairCode-biltrans sentences.txt biltrans.txt

For example
apertium -d $HOME/apertium-kaz-tur kaz-tur-biltrans sentences.txt biltrans.txt

To apply the apertium tool lextor on the output of the biltrans:

lrx-proc -m $HOME/apertium-kaz-tur/kaz-tur.autolex.bin inFilePath > outFilePath

For example
lrx-proc -m $HOME/apertium-kaz-tur/kaz-tur.autolex.bin biltrans.txt > lextor.txt

To run rules-applier program

./rules-applier localeId transferFile.t1x sentences.txt lextor.txt rulesOut.txt

For example
./rules-applier kk_KZ $HOME/transferFile.t1x sentences.txt lextor.txt rulesOut.txt

localeId= ICU localeId for the source language, sentences.txt= source language sentences, rulesOut.txt= output file of your results 

To apply the apertium tool interchunk into rulesOut.txt file:

apertium-interchunk $pairPar/apertium-$pairCode/apertium-$pairCode.$pairCode.t2x $pairPar/apertium-$pairCode/$pairCode.t2x.bin rulesOut.txt interchunk.txt

For example
apertium-interchunk $HOME/apertium-kaz-tur/apertium-kaz-tur.kaz-tur.t2x $HOME/apertium-kaz-tur/kaz-tur.t2x.bin rulesOut.txt interchunk.txt

To apply the apertium tool postchunk to the interchunk output file:

apertium-postchunk $pairPar/apertium-$pairCode/apertium-$pairCode.$pairCode.t3x $pairPar/apertium-$pairCode/$pairCode.t3x.bin interchunk.txt postchunk.txt

For example
apertium-postchunk $HOME/apertium-kaz-tur/apertium-kaz-tur.kaz-tur.t3x $HOME/apertium-kaz-tur/kaz-tur.t3x.bin interchunk.txt postchunk.txt

To apply the apertium tool "transfer" to the "postchunk" output file

1. INPUT: Outputof the postchunk module
2. OUTPUT: Morphologically generated sentences in the target language

apertium-transfer -n $pairPar/apertium-$pairCode/apertium-$pairCode.$pairCode.t4x $pairPar/apertium-$pairCode/$pairCode.t4x.bin postchunk.txt | lt-proc -g $pairPar/apertium-$pairCode/$pairCode.autogen.bin | lt-proc -p $pairPar/apertium-$pairCode/$pairCode.autopgen.bin > transfer.txt

For example
apertium-transfer -n $HOME/apertium-kaz-tur/apertium-kaz-tur.kaz-tur.t4x $HOME/apertium-kaz-tur/kaz-tur.t4x.bin postchunk.txt | lt-proc -g $HOME/apertium-kaz-tur/kaz-tur.autogen.bin | lt-proc -p $HOME/apertium-kaz-tur/kaz-tur.autopgen.bin > transfer.txt

Install and build kenlm

Download and install kenlm by the following steps under 'USAGE' at https://kheafield.com/code/kenlm/

Download a big Turkish corpus from wikidumps:

$ wget https://dumps.wikimedia.org/trwikinews/20181020/trwikinews-20181020-pages-articles.xml.bz2. 

For training, you should follow these steps:

1. Estimating: run bin/lmplz -o 5 <text >text.arpa
2. Querying: run bin/build_binary text.arpa text.binary

Python script (score-sentences.py) used to score target language's sentences with language model, it can be found at https://github.com/sevilaybayatli/apertium-ambiguous/tree/master/scripts.

For running model weight program on the transfer file as in bash script:

python3 $modelWeight $LM < transfer.txt > weights.txt

For example
python3 score-sentences.py kenlm < transfer.txt > weights.txt

Install and build yasmet

Downloading and compiling yasmet by doing the following:

Download yasmet either from https://www-i6.informatik.rwth-aachen.de/web/Software/YASMET.html or from https://github.com/apertium/apertium-lex-tools/blob/master/yasmet.cc

To build and compile, follow steps below:

1. g++ -o yasmet yasmet.cc
2. ./yasmet

$mv yasmet ./apertium-ambiguous

Running yasmet-formatter to prepare yasmet datasets. Also this will generate the analysis output file , beside the best model weighting translations(scoring with language model) in file modelWeight.txt, and random translations(choosing applying rule randomly form transfer file) in file randomWeight.txt.

To apply yasmet-formatter program

./yasmet-formatter $localeId sentences.txt lextor.txt transfer.txt weights.txt $outputFile $datasets

For example
./yasmet-formatter kk-KZ sentences.txt lextor.txt transfer.txt weights.txt sentences.out datasets

Training and Testing apertium-ambiguous

Training should be done by running Generate the yasmet datasets, actually running the command

bash training-yasmet.sh kk test1.txt kaz-tur $HOME/score-sentences.py kk_KZ test1.out datasets

Generate-yasmet-models.sh Generate the yasmet models from the yasmet datasets, actually running the command

bash generate-yasmet-models.sh datasets models

Testing can be done by running

Running beam search with beam = beam_number in the sentencesFile , writing its results in file "BeamSearch-k.txt".

./beam-search localeId sentencesFile lextorFile transferOutFile modelsFolder localeId k1 k2 k3 ...

For example 
./beam-search kk_KZ tranferFile.tx1 test.txt lextor.txt transfer.txt models 2 4 8 10 

test.txt= test.text(source language text(Kazakh)), output-file= BeamSearch-2.txt, BeamSearch-4.txt.., and k= 2 4 8 10 or any number.

Enjoy using apertium-ambiguous :)