@inproceedings{yilmaz-etal-2013-tubitak,
title = {{T{\"U}B{\.I}TAK} {T}urkish-{E}nglish submissions for {IWSLT} 2013},
author = {Y{\i}lmaz, Ertu{\u{g}}rul and
El-Kahlout, {\.I}lknur Durgar and
Ayd{\i}n, Burak and
{\"O}zil, Zi{\c{s}}an S{\i}la and
Mermer, Co{\c{s}}kun},
editor = "Zhang, Joy Ying",
booktitle = "Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign",
month = dec # " 5-6",
year = "2013",
address = "Heidelberg, Germany",
url = "https://aclanthology.org/2013.iwslt-evaluation.19",
abstract = "This paper describes the TU ̈ B ̇ITAK Turkish-English submissions in both directions for the IWSLT{'}13 Evaluation Campaign TED Machine Translation (MT) track. We develop both phrase-based and hierarchical phrase-based statistical machine translation (SMT) systems based on Turkish wordand morpheme-level representations. We augment training data with content words extracted from itself and experiment with reverse word order for source languages. For the Turkish-to-English direction, we use Gigaword corpus as an additional language model with the training data. For the English-to-Turkish direction, we implemented a wide coverage Turkish word generator to generate words from the stem and morpheme sequences. Finally, we perform system combination of the different systems produced with different word alignments.",
}
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<abstract>This paper describes the TU ̈ B ̇ITAK Turkish-English submissions in both directions for the IWSLT’13 Evaluation Campaign TED Machine Translation (MT) track. We develop both phrase-based and hierarchical phrase-based statistical machine translation (SMT) systems based on Turkish wordand morpheme-level representations. We augment training data with content words extracted from itself and experiment with reverse word order for source languages. For the Turkish-to-English direction, we use Gigaword corpus as an additional language model with the training data. For the English-to-Turkish direction, we implemented a wide coverage Turkish word generator to generate words from the stem and morpheme sequences. Finally, we perform system combination of the different systems produced with different word alignments.</abstract>
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%0 Conference Proceedings
%T TÜBİTAK Turkish-English submissions for IWSLT 2013
%A Yılmaz, Ertuğrul
%A El-Kahlout, İlknur Durgar
%A Aydın, Burak
%A Özil, Zişan Sıla
%A Mermer, Coşkun
%Y Zhang, Joy Ying
%S Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign
%D 2013
%8 dec 5 6
%C Heidelberg, Germany
%F yilmaz-etal-2013-tubitak
%X This paper describes the TU ̈ B ̇ITAK Turkish-English submissions in both directions for the IWSLT’13 Evaluation Campaign TED Machine Translation (MT) track. We develop both phrase-based and hierarchical phrase-based statistical machine translation (SMT) systems based on Turkish wordand morpheme-level representations. We augment training data with content words extracted from itself and experiment with reverse word order for source languages. For the Turkish-to-English direction, we use Gigaword corpus as an additional language model with the training data. For the English-to-Turkish direction, we implemented a wide coverage Turkish word generator to generate words from the stem and morpheme sequences. Finally, we perform system combination of the different systems produced with different word alignments.
%U https://aclanthology.org/2013.iwslt-evaluation.19
Markdown (Informal)
[TÜBİTAK Turkish-English submissions for IWSLT 2013](https://aclanthology.org/2013.iwslt-evaluation.19) (Yılmaz et al., IWSLT 2013)
ACL
- Ertuğrul Yılmaz, İlknur Durgar El-Kahlout, Burak Aydın, Zişan Sıla Özil, and Coşkun Mermer. 2013. TÜBİTAK Turkish-English submissions for IWSLT 2013. In Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign, Heidelberg, Germany.