A Bayesian model for joint word alignment and part-of-speech transfer

Current methods for word alignment require considerable amounts of
parallel text to deliver accurate results, a requirement which is met only for
a small minority of the world's approximately 7,000 languages.
We show that by jointly performing word alignment and annotation transfer in
a novel Bayesian model, alignment accuracy can be
improved for language pairs where annotations are available for only
one of the languages---a finding which could facilitate the study and
processing of a vast number of low-resource languages.
We also present an evaluation where our method is used to perform
single-source and multi-source part-of-speech transfer with 22 translations
of the same text in four different languages. This allows us to quantify the
considerable variation in accuracy depending on the specific source text(s)
used, even with different translations into the same language.