Publication

Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data

Toral Ruiz, A., Edman, L., Spenader, J. & Yeshmagambetova, G., 1-Aug-2019, Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1). Forence, Italy: Association for Computational Linguistics (ACL), Vol. 2. p. 386-392 7 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

APA

Toral Ruiz, A., Edman, L., Spenader, J., & Yeshmagambetova, G. (2019). Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1) (Vol. 2, pp. 386-392). Association for Computational Linguistics (ACL).

Author

Toral Ruiz, Antonio ; Edman, Lukas ; Spenader, Jennifer ; Yeshmagambetova, Galiya. / Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data. Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1). Vol. 2 Forence, Italy : Association for Computational Linguistics (ACL), 2019. pp. 386-392

Harvard

Toral Ruiz, A, Edman, L, Spenader, J & Yeshmagambetova, G 2019, Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data. in Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1). vol. 2, Association for Computational Linguistics (ACL), Forence, Italy, pp. 386-392.

Standard

Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data. / Toral Ruiz, Antonio; Edman, Lukas; Spenader, Jennifer; Yeshmagambetova, Galiya.

Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1). Vol. 2 Forence, Italy : Association for Computational Linguistics (ACL), 2019. p. 386-392.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Vancouver

Toral Ruiz A, Edman L, Spenader J, Yeshmagambetova G. Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1). Vol. 2. Forence, Italy: Association for Computational Linguistics (ACL). 2019. p. 386-392


BibTeX

@inproceedings{66a779dd87734d94837f5e3e3d5dbce1,
title = "Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data",
abstract = "This paper presents the systems submitted by the University of Groningen to the English-Kazakh language pair (both translation directions) for the WMT 2019 news translation task. We explore the potential benefits of (i) morphological segmentation (both unsupervised and rule-based), given the agglutinative nature of Kazakh, (ii) data from two additional languages (Turkish and Russian), given the scarcity of English-Kazakh data and (iii) synthetic data, both for the source and for the target language. Our best sub- missions ranked second for Kazakh-English and third for English-Kazakh in terms of the BLEU automatic evaluation metric.",
author = "{Toral Ruiz}, Antonio and Lukas Edman and Jennifer Spenader and Galiya Yeshmagambetova",
year = "2019",
month = aug,
day = "1",
language = "English",
volume = "2",
pages = "386--392",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
publisher = "Association for Computational Linguistics (ACL)",

}

RIS

TY - GEN

T1 - Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data

AU - Toral Ruiz, Antonio

AU - Edman, Lukas

AU - Spenader, Jennifer

AU - Yeshmagambetova, Galiya

PY - 2019/8/1

Y1 - 2019/8/1

N2 - This paper presents the systems submitted by the University of Groningen to the English-Kazakh language pair (both translation directions) for the WMT 2019 news translation task. We explore the potential benefits of (i) morphological segmentation (both unsupervised and rule-based), given the agglutinative nature of Kazakh, (ii) data from two additional languages (Turkish and Russian), given the scarcity of English-Kazakh data and (iii) synthetic data, both for the source and for the target language. Our best sub- missions ranked second for Kazakh-English and third for English-Kazakh in terms of the BLEU automatic evaluation metric.

AB - This paper presents the systems submitted by the University of Groningen to the English-Kazakh language pair (both translation directions) for the WMT 2019 news translation task. We explore the potential benefits of (i) morphological segmentation (both unsupervised and rule-based), given the agglutinative nature of Kazakh, (ii) data from two additional languages (Turkish and Russian), given the scarcity of English-Kazakh data and (iii) synthetic data, both for the source and for the target language. Our best sub- missions ranked second for Kazakh-English and third for English-Kazakh in terms of the BLEU automatic evaluation metric.

UR - http://www.aclweb.org/anthology/W19-5343

M3 - Conference contribution

VL - 2

SP - 386

EP - 392

BT - Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

PB - Association for Computational Linguistics (ACL)

CY - Forence, Italy

ER -

ID: 95754758