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

Robert Östling

Research output: Chapter in Book/Report/Conference proceedingChapterScientific

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Number of pages10
Place of PublicationOsaka, Japan
PublisherThe Association for Computational Linguistics
Publication date1 Dec 2016
Pages620-629
ISBN (Electronic)978-4-87974-702-0
Publication statusPublished - 1 Dec 2016
MoE publication typeB2 Book chapter

Publication series

NameProceedings of COLING
ISSN (Print)1525-2477

Fields of Science

  • 6121 Languages

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