Machine translation for language preservation

Steven Bird; David Chiang

Machine translation for language preservation

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review

Abstract

Statistical machine translation has been remarkably successful for the world’s well-resourced languages, and much effort is focussed on creating and exploiting rich resources such as treebanks and wordnets. Machine translation can also support the urgent task of documenting the world’s endangered languages. The primary object of statistical translation models, bilingual aligned text, closely coincides with interlinear text, the primary artefact collected in
documentary linguistics. It ought to be possible to exploit this similarity in order to improve the quantity and quality of documentation for a language. Yet there are many technical and logistical problems to be addressed, starting with the problem that – for most of the languages in question – no texts or lexicons exist. In this position paper, we examine these challenges, and report on a data collection effort involving 15 endangered languages spoken in the highlands of
Papua New Guinea.

Original language	English
Title of host publication	Proceedings of the 24th International Conference on Computational Linguistics
Pages	125-134
Number of pages	10
Publication status	Published - 2012
Externally published	Yes
Event	International Conference on Computational Linguistics - Mumbai, India Duration: 8 Dec 2012 → 15 Dec 2012 Conference number: 24th

Conference

Conference	International Conference on Computational Linguistics
Abbreviated title	COLING 2012
Country/Territory	India
City	Mumbai
Period	8/12/12 → 15/12/12

Cite this

@inproceedings{0c2c7ff3826a4adc9b75bcf8c9a15e01,

title = "Machine translation for language preservation",

abstract = "Statistical machine translation has been remarkably successful for the world{\textquoteright}s well-resourced languages, and much effort is focussed on creating and exploiting rich resources such as treebanks and wordnets. Machine translation can also support the urgent task of documenting the world{\textquoteright}s endangered languages. The primary object of statistical translation models, bilingual aligned text, closely coincides with interlinear text, the primary artefact collected indocumentary linguistics. It ought to be possible to exploit this similarity in order to improve the quantity and quality of documentation for a language. Yet there are many technical and logistical problems to be addressed, starting with the problem that – for most of the languages in question – no texts or lexicons exist. In this position paper, we examine these challenges, and report on a data collection effort involving 15 endangered languages spoken in the highlands ofPapua New Guinea.",

author = "Steven Bird and David Chiang",

year = "2012",

language = "English",

pages = "125--134",

booktitle = "Proceedings of the 24th International Conference on Computational Linguistics",

note = "International Conference on Computational Linguistics, COLING 2012 ; Conference date: 08-12-2012 Through 15-12-2012",

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T1 - Machine translation for language preservation

AU - Bird, Steven

AU - Chiang, David

N1 - Conference code: 24th

PY - 2012

Y1 - 2012

N2 - Statistical machine translation has been remarkably successful for the world’s well-resourced languages, and much effort is focussed on creating and exploiting rich resources such as treebanks and wordnets. Machine translation can also support the urgent task of documenting the world’s endangered languages. The primary object of statistical translation models, bilingual aligned text, closely coincides with interlinear text, the primary artefact collected indocumentary linguistics. It ought to be possible to exploit this similarity in order to improve the quantity and quality of documentation for a language. Yet there are many technical and logistical problems to be addressed, starting with the problem that – for most of the languages in question – no texts or lexicons exist. In this position paper, we examine these challenges, and report on a data collection effort involving 15 endangered languages spoken in the highlands ofPapua New Guinea.

AB - Statistical machine translation has been remarkably successful for the world’s well-resourced languages, and much effort is focussed on creating and exploiting rich resources such as treebanks and wordnets. Machine translation can also support the urgent task of documenting the world’s endangered languages. The primary object of statistical translation models, bilingual aligned text, closely coincides with interlinear text, the primary artefact collected indocumentary linguistics. It ought to be possible to exploit this similarity in order to improve the quantity and quality of documentation for a language. Yet there are many technical and logistical problems to be addressed, starting with the problem that – for most of the languages in question – no texts or lexicons exist. In this position paper, we examine these challenges, and report on a data collection effort involving 15 endangered languages spoken in the highlands ofPapua New Guinea.

M3 - Conference Paper published in Proceedings

SP - 125

EP - 134

BT - Proceedings of the 24th International Conference on Computational Linguistics

T2 - International Conference on Computational Linguistics

Y2 - 8 December 2012 through 15 December 2012

ER -