CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks

Jindřich Libovický; Jindřich Helcl; Marek Tlustý; Ondřej Bojar; Pavel Pecina

doi:10.18653/v1/W16-2361

CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks

Jindřich Libovický, Jindřich Helcl, Marek Tlustý, Ondřej Bojar, Pavel Pecina

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

Abstract / Description of output

Neural sequence to sequence learning recently became a very promising paradigmin machine translation, achieving competitive results with statistical phrase-based systems. In this system description paper, we attempt to utilize several recently published methods used for neural sequential learning in order to build systems for WMT 2016 shared tasks of Automatic Post-Editing and Multimodal Machine Translation.

Original language	English
Title of host publication	Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers
Place of Publication	Berlin, Germany
Publisher	Association for Computational Linguistics
Pages	646-654
Number of pages	9
DOIs	https://doi.org/10.18653/v1/W16-2361
Publication status	Published - 11 Aug 2016
Event	First Conference on Machine Translation - Berlin, Germany Duration: 11 Aug 2016 → 12 Aug 2016 http://www.statmt.org/wmt16/

Conference

Conference	First Conference on Machine Translation
Abbreviated title	WMT16
Country/Territory	Germany
City	Berlin
Period	11/08/16 → 12/08/16
Internet address	http://www.statmt.org/wmt16/

Access to Document

10.18653/v1/W16-2361Licence: Creative Commons: Attribution (CC-BY)

CUNI System for WMT16_LIBOVICKY_DOA05062016_VOR_CC-BYFinal published version, 1.05 MBLicence: Creative Commons: Attribution (CC-BY)

https://www.aclweb.org/anthology/W16-2361Licence: Creative Commons: Attribution (CC-BY)

Cite this

@inproceedings{6f09cc25516a4fb2b9c216a376e74d74,

title = "CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks",

abstract = "Neural sequence to sequence learning recently became a very promising paradigmin machine translation, achieving competitive results with statistical phrase-based systems. In this system description paper, we attempt to utilize several recently published methods used for neural sequential learning in order to build systems for WMT 2016 shared tasks of Automatic Post-Editing and Multimodal Machine Translation.",

author = "Jind{\v r}ich Libovick{\'y} and Jind{\v r}ich Helcl and Marek Tlust{\'y} and Ond{\v r}ej Bojar and Pavel Pecina",

year = "2016",

month = aug,

day = "11",

doi = "10.18653/v1/W16-2361",

language = "English",

pages = "646--654",

booktitle = "Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers",

publisher = "Association for Computational Linguistics",

note = "First Conference on Machine Translation, WMT16 ; Conference date: 11-08-2016 Through 12-08-2016",

url = "http://www.statmt.org/wmt16/",

}

Libovický, J, Helcl, J, Tlustý, M, Bojar, O & Pecina, P 2016, CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks. in Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers. Association for Computational Linguistics, Berlin, Germany, pp. 646-654, First Conference on Machine Translation, Berlin, Germany, 11/08/16. https://doi.org/10.18653/v1/W16-2361

CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks. / Libovický, Jindřich; Helcl, Jindřich; Tlustý, Marek et al.
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers. Berlin, Germany: Association for Computational Linguistics, 2016. p. 646-654.

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

TY - GEN

T1 - CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks

AU - Libovický, Jindřich

AU - Helcl, Jindřich

AU - Tlustý, Marek

AU - Bojar, Ondřej

AU - Pecina, Pavel

PY - 2016/8/11

Y1 - 2016/8/11

N2 - Neural sequence to sequence learning recently became a very promising paradigmin machine translation, achieving competitive results with statistical phrase-based systems. In this system description paper, we attempt to utilize several recently published methods used for neural sequential learning in order to build systems for WMT 2016 shared tasks of Automatic Post-Editing and Multimodal Machine Translation.

AB - Neural sequence to sequence learning recently became a very promising paradigmin machine translation, achieving competitive results with statistical phrase-based systems. In this system description paper, we attempt to utilize several recently published methods used for neural sequential learning in order to build systems for WMT 2016 shared tasks of Automatic Post-Editing and Multimodal Machine Translation.

U2 - 10.18653/v1/W16-2361

DO - 10.18653/v1/W16-2361

M3 - Conference contribution

SP - 646

EP - 654

BT - Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

PB - Association for Computational Linguistics

CY - Berlin, Germany

T2 - First Conference on Machine Translation

Y2 - 11 August 2016 through 12 August 2016

ER -

CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks

Abstract / Description of output

Conference

Access to Document

Fingerprint

Cite this