@inproceedings{banerjee-etal-2011-dcu,
title = "The {DCU} machine translation systems for {IWSLT} 2011",
author = "Banerjee, Pratyush and
Almaghout, Hala and
Naskar, Sudip and
Roturier, Johann and
Jiang, Jie and
Way, Andy and
van Genabith, Josef",
editor = {Federico, Marcello and
Hwang, Mei-Yuh and
R{\"o}dder, Margit and
St{\"u}ker, Sebastian},
booktitle = "Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign",
month = dec # " 8-9",
year = "2011",
address = "San Francisco, California",
url = "https://aclanthology.org/2011.iwslt-evaluation.4",
pages = "41--48",
abstract = "In this paper, we provide a description of the Dublin City University{'}s (DCU) submissions in the IWSLT 2011 evaluationcampaign.1 WeparticipatedintheArabic-Englishand Chinese-English Machine Translation(MT) track translation tasks. We use phrase-based statistical machine translation (PBSMT) models to create the baseline system. Due to the open-domain nature of the data to be translated, we use domain adaptation techniques to improve the quality of translation. Furthermore, we explore target-side syntactic augmentation for an Hierarchical Phrase-Based (HPB) SMT model. Combinatory Categorial Grammar (CCG) is used to extract labels for target-side phrases and non-terminals in the HPB system. Combining the domain adapted language models with the CCG-augmented HPB system gave us the best translations for both language pairs providing statistically significant improvements of 6.09 absolute BLEU points (25.94{\%} relative) and 1.69 absolute BLEU points (15.89{\%} relative) over the unadapted PBSMT baselines for the Arabic-English and Chinese-English language pairs, respectively.",
}
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<abstract>In this paper, we provide a description of the Dublin City University’s (DCU) submissions in the IWSLT 2011 evaluationcampaign.1 WeparticipatedintheArabic-Englishand Chinese-English Machine Translation(MT) track translation tasks. We use phrase-based statistical machine translation (PBSMT) models to create the baseline system. Due to the open-domain nature of the data to be translated, we use domain adaptation techniques to improve the quality of translation. Furthermore, we explore target-side syntactic augmentation for an Hierarchical Phrase-Based (HPB) SMT model. Combinatory Categorial Grammar (CCG) is used to extract labels for target-side phrases and non-terminals in the HPB system. Combining the domain adapted language models with the CCG-augmented HPB system gave us the best translations for both language pairs providing statistically significant improvements of 6.09 absolute BLEU points (25.94% relative) and 1.69 absolute BLEU points (15.89% relative) over the unadapted PBSMT baselines for the Arabic-English and Chinese-English language pairs, respectively.</abstract>
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%0 Conference Proceedings
%T The DCU machine translation systems for IWSLT 2011
%A Banerjee, Pratyush
%A Almaghout, Hala
%A Naskar, Sudip
%A Roturier, Johann
%A Jiang, Jie
%A Way, Andy
%A van Genabith, Josef
%Y Federico, Marcello
%Y Hwang, Mei-Yuh
%Y Rödder, Margit
%Y Stüker, Sebastian
%S Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign
%D 2011
%8 dec 8 9
%C San Francisco, California
%F banerjee-etal-2011-dcu
%X In this paper, we provide a description of the Dublin City University’s (DCU) submissions in the IWSLT 2011 evaluationcampaign.1 WeparticipatedintheArabic-Englishand Chinese-English Machine Translation(MT) track translation tasks. We use phrase-based statistical machine translation (PBSMT) models to create the baseline system. Due to the open-domain nature of the data to be translated, we use domain adaptation techniques to improve the quality of translation. Furthermore, we explore target-side syntactic augmentation for an Hierarchical Phrase-Based (HPB) SMT model. Combinatory Categorial Grammar (CCG) is used to extract labels for target-side phrases and non-terminals in the HPB system. Combining the domain adapted language models with the CCG-augmented HPB system gave us the best translations for both language pairs providing statistically significant improvements of 6.09 absolute BLEU points (25.94% relative) and 1.69 absolute BLEU points (15.89% relative) over the unadapted PBSMT baselines for the Arabic-English and Chinese-English language pairs, respectively.
%U https://aclanthology.org/2011.iwslt-evaluation.4
%P 41-48
Markdown (Informal)
[The DCU machine translation systems for IWSLT 2011](https://aclanthology.org/2011.iwslt-evaluation.4) (Banerjee et al., IWSLT 2011)
ACL
- Pratyush Banerjee, Hala Almaghout, Sudip Naskar, Johann Roturier, Jie Jiang, Andy Way, and Josef van Genabith. 2011. The DCU machine translation systems for IWSLT 2011. In Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign, pages 41–48, San Francisco, California.