Learning rules from parallel and non-parallel corpora

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Estimating rules using parallel corpora

It is always recommended to use a parallel corpus for any type of machine translation training when such a resource is available. This section describes several (3) methods for estimating lexical selection rules using a parallel corpus. We start by describing the part of the training process that is shared by all three methods, and then continue to describe each of the individual methods separately.


The training methods use several software packages that need to be installed. First you will need to download and install:

Furthermore you will also need:

  • an Apertium language pair
  • a parallel corpus (see Corpora)

Preparing the training files

Estimating rules using non-parallel corpora


as a reference on how to add these modes if they do not exist. Place the following Makefile in the folder where you want to run your training process:


#all: data/$(CORPUS).$(DIR).lrx data/$(CORPUS).$(DIR).freq.lrx
all: data/$(CORPUS).$(DIR).freq.lrx.bin data/$(CORPUS).$(DIR).patterns.lrx

data/$(CORPUS).$(DIR).tagger: $(CORPUS).$(DIR).txt
	if [ ! -d data ]; then mkdir data; fi
	cat $(CORPUS).$(DIR).txt | sed 's/[^\.]$$/./g' | apertium-destxt | apertium -f none -d $(DATA) $(DIR)-tagger | apertium-pretransfer > $@
data/$(CORPUS).$(DIR).ambig: data/$(CORPUS).$(DIR).tagger
	cat data/$(CORPUS).$(DIR).tagger | $(LEX_TOOLS)/multitrans $(DATA)$(DIR).autobil.bin -b -t > $@

data/$(CORPUS).$(DIR).multi-trimmed: data/$(CORPUS).$(DIR).tagger
	cat data/$(CORPUS).$(DIR).tagger | $(LEX_TOOLS)/multitrans $(DATA)$(DIR).autobil.bin -m -t > $@

data/$(CORPUS).$(DIR).ranked: data/$(CORPUS).$(DIR).tagger
	cat $< | $(LEX_TOOLS)/multitrans $(DATA)$(DIR).autobil.bin -m | apertium -f none -d $(DATA) $(DIR)-multi | irstlm-ranker-frac $(MODEL) > $@

data/$(CORPUS).$(DIR).annotated: data/$(CORPUS).$(DIR).multi-trimmed data/$(CORPUS).$(DIR).ranked
	paste data/$(CORPUS).$(DIR).multi-trimmed data/$(CORPUS).$(DIR).ranked | cut -f1-4 > $@
data/$(CORPUS).$(DIR).freq: data/$(CORPUS).$(DIR).ambig data/$(CORPUS).$(DIR).annotated
	python3 $(SCRIPTS)/biltrans-extract-frac-freq.py  data/$(CORPUS).$(DIR).ambig data/$(CORPUS).$(DIR).annotated > $@
data/$(CORPUS).$(DIR).freq.lrx:  data/$(CORPUS).$(DIR).freq
	python3 $(SCRIPTS)/extract-alig-lrx.py $< > $@

data/$(CORPUS).$(DIR).freq.lrx.bin: data/$(CORPUS).$(DIR).freq.lrx
	lrx-comp $< $@

data/$(CORPUS).$(DIR).ngrams: data/$(CORPUS).$(DIR).freq data/$(CORPUS).$(DIR).ambig data/$(CORPUS).$(DIR).annotated
	python3 $(SCRIPTS)/biltrans-count-patterns-ngrams.py data/$(CORPUS).$(DIR).freq data/$(CORPUS).$(DIR).ambig data/$(CORPUS).$(DIR).annotated > $@
data/$(CORPUS).$(DIR).patterns: data/$(CORPUS).$(DIR).freq data/$(CORPUS).$(DIR).ngrams
	python3 $(SCRIPTS)/ngram-pruning-frac.py data/$(CORPUS).$(DIR).freq data/$(CORPUS).$(DIR).ngrams > $@  
data/$(CORPUS).$(DIR).patterns.lrx:  data/$(CORPUS).$(DIR).patterns
	python3 $(SCRIPTS)/ngrams-to-rules.py $< $(THR) > $@

In the same folder also place your source side corpus file. The corpus file needs to be named as "basename"."language-pair".txt.
As an illustration, in the Makefile example, the corpus file is named setimes.sh-mk.txt.

Set the Makefile variables as follows:

  • CORPUS denotes the base name of your corpus file
  • DIR stands for the language pair
  • DATA is the path to the language resources for the language pair
  • AUTOBIL is the path to binary bilingual dictionary for the language pair
  • SCRIPTS denotes the path to the lex-tools scripts
  • MODEL is the path to the target side (binary) language model used for scoring the possible translations of ambiguous words

Finally, executing the Makefile will generate lexical selection rules for the specified language pair.