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

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › 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.

Original language	English
Title of host publication	Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Publication date	9 Aug 2013
Publication status	Published - 9 Aug 2013
Externally published	Yes

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Cite this

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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.",

author = "Christian Hardmeier and Sara Stymne and J{\"o}rg Tiedemann and Joakim Nivre",

year = "2013",

month = aug,

day = "9",

language = "English",

booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations",

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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.

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › 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.

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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