Docent: A Document-Level Decoder for Phrase-Based Statistical Machine Translation

Christian Hardmeier; Sara Stymne; Jörg Tiedemann; Joakim Nivre

Docent: A Document-Level Decoder for Phrase-Based Statistical Machine Translation

Christian Hardmeier, Sara Stymne, Jörg Tiedemann, Joakim Nivre

Uppsala University

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

Abstract

We describe Docent, an open-source decoder for statistical machine translation that breaks with the usual sentence-by-sentence paradigm and translates complete documents as units. By taking translation to the document level, our decoder can handle feature models with arbitrary discourse-wide dependencies and constitutes an essential infrastructure component in the quest for discourse-aware SMT models.

Originalsprog	Engelsk
Titel	Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Publikationsdato	9 aug. 2013
Status	Udgivet - 9 aug. 2013
Udgivet eksternt	Ja

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https://www.diva-portal.org/smash/get/diva2:649088/fulltext01.pdf

Citationsformater

@inproceedings{50bc26ceebf54e15a2f8bcecf53276ef,

title = "Docent: A Document-Level Decoder for Phrase-Based Statistical Machine Translation",

abstract = "We describe Docent, an open-source decoder for statistical machine translation that breaks with the usual sentence-by-sentence paradigm and translates complete documents as units. By taking translation to the document level, our decoder can handle feature models with arbitrary discourse-wide dependencies and constitutes an essential infrastructure component in the quest for discourse-aware SMT models.",

keywords = "Document-level translation, Statistical machine translation, Discourse-aware SMT, Feature models for SMT, Open-source decoder",

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year = "2013",

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Docent: A Document-Level Decoder for Phrase-Based Statistical Machine Translation. / Hardmeier, Christian; Stymne, Sara; Tiedemann, Jörg et al.
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 2013.

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

TY - GEN

T1 - Docent: A Document-Level Decoder for Phrase-Based Statistical Machine Translation

AU - Hardmeier, Christian

AU - Stymne, Sara

AU - Tiedemann, Jörg

AU - Nivre, Joakim

PY - 2013/8/9

Y1 - 2013/8/9

N2 - We describe Docent, an open-source decoder for statistical machine translation that breaks with the usual sentence-by-sentence paradigm and translates complete documents as units. By taking translation to the document level, our decoder can handle feature models with arbitrary discourse-wide dependencies and constitutes an essential infrastructure component in the quest for discourse-aware SMT models.

AB - We describe Docent, an open-source decoder for statistical machine translation that breaks with the usual sentence-by-sentence paradigm and translates complete documents as units. By taking translation to the document level, our decoder can handle feature models with arbitrary discourse-wide dependencies and constitutes an essential infrastructure component in the quest for discourse-aware SMT models.

KW - Document-level translation

KW - Statistical machine translation

KW - Discourse-aware SMT

KW - Feature models for SMT

KW - Open-source decoder

M3 - Article in proceedings

BT - Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

ER -

Docent: A Document-Level Decoder for Phrase-Based Statistical Machine Translation

Abstract

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