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        <title>A Bayesian model for joint word alignment and part-of-speech transfer</title>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T A Bayesian model for joint word alignment and part-of-speech transfer
%A Östling, Robert
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F ostling-2016-bayesian
%X 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.
%U https://aclanthology.org/C16-1060
%P 620-629

Markdown (Informal)

[A Bayesian model for joint word alignment and part-of-speech transfer](https://aclanthology.org/C16-1060) (Östling, COLING 2016)

• A Bayesian model for joint word alignment and part-of-speech transfer (Östling, COLING 2016)

ACL

• Robert Östling. 2016. A Bayesian model for joint word alignment and part-of-speech transfer. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 620–629, Osaka, Japan. The COLING 2016 Organizing Committee.