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: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

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
Place of Publication	Sofia, Bulgaria
Publisher	Association for Computational Linguistics
Pages	193-198
Number of pages	6
Publication status	Published - 9 Aug 2013
Event	51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 - Sofia, Bulgaria Duration: 4 Aug 2013 → 9 Aug 2013

Conference

Conference	51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
Country/Territory	Bulgaria
City	Sofia
Period	4/08/13 → 9/08/13

Access to Document

Docent A Document-level_HARDMEIER_DOA13052013_VOR_CC-BYFinal published version, 155 KBLicence: Creative Commons: Attribution-NonCommercial-ShareAlike (CC BY-NC-SA)

https://www.aclweb.org/anthology/P13-4033Licence: Creative Commons: Attribution-NonCommercial-ShareAlike (CC BY-NC-SA)

Cite this

@inproceedings{99029f0770b0498e84da3677846d8f8f,

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",

pages = "193--198",

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

publisher = "Association for Computational Linguistics",

note = "51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 ; Conference date: 04-08-2013 Through 09-08-2013",

}

Hardmeier, C, Stymne, S, Tiedemann, J & Nivre, J 2013, Docent: A Document-Level Decoder for Phrase-Based Statistical Machine Translation. in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Association for Computational Linguistics, Sofia, Bulgaria, pp. 193-198, 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013, Sofia, Bulgaria, 4/08/13. <https://www.aclweb.org/anthology/P13-4033>

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. Sofia, Bulgaria: Association for Computational Linguistics, 2013. p. 193-198.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 - Conference contribution

SP - 193

EP - 198

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

PB - Association for Computational Linguistics

CY - Sofia, Bulgaria

T2 - 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

Y2 - 4 August 2013 through 9 August 2013

ER -

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

Abstract / Description of output

Conference

Access to Document

Fingerprint

Cite this