@inproceedings{ng-etal-2014-usfd,
title = "The {USFD} {SLT} system for {IWSLT} 2014",
author = "Ng, Raymond W. M. and
Doulaty, Mortaza and
Doddipatla, Rama and
Aziz, Wilker and
Shah, Kashif and
Saz, Oscar and
Hasan, Madina and
AlHaribi, Ghada and
Specia, Lucia and
Hain, Thomas",
editor = {Federico, Marcello and
St{\"u}ker, Sebastian and
Yvon, Fran{\c{c}}ois},
booktitle = "Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign",
month = dec # " 4-5",
year = "2014",
address = "Lake Tahoe, California",
url = "https://aclanthology.org/2014.iwslt-evaluation.11",
pages = "86--91",
abstract = "The University of Sheffield (USFD) participated in the International Workshop for Spoken Language Translation (IWSLT) in 2014. In this paper, we will introduce the USFD SLT system for IWSLT. Automatic speech recognition (ASR) is achieved by two multi-pass deep neural network systems with adaptation and rescoring techniques. Machine translation (MT) is achieved by a phrase-based system. The USFD primary system incorporates state-of-the-art ASR and MT techniques and gives a BLEU score of 23.45 and 14.75 on the English-to-French and English-to-German speech-to-text translation task with the IWSLT 2014 data. The USFD contrastive systems explore the integration of ASR and MT by using a quality estimation system to rescore the ASR outputs, optimising towards better translation. This gives a further 0.54 and 0.26 BLEU improvement respectively on the IWSLT 2012 and 2014 evaluation data.",
}
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<abstract>The University of Sheffield (USFD) participated in the International Workshop for Spoken Language Translation (IWSLT) in 2014. In this paper, we will introduce the USFD SLT system for IWSLT. Automatic speech recognition (ASR) is achieved by two multi-pass deep neural network systems with adaptation and rescoring techniques. Machine translation (MT) is achieved by a phrase-based system. The USFD primary system incorporates state-of-the-art ASR and MT techniques and gives a BLEU score of 23.45 and 14.75 on the English-to-French and English-to-German speech-to-text translation task with the IWSLT 2014 data. The USFD contrastive systems explore the integration of ASR and MT by using a quality estimation system to rescore the ASR outputs, optimising towards better translation. This gives a further 0.54 and 0.26 BLEU improvement respectively on the IWSLT 2012 and 2014 evaluation data.</abstract>
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%0 Conference Proceedings
%T The USFD SLT system for IWSLT 2014
%A Ng, Raymond W. M.
%A Doulaty, Mortaza
%A Doddipatla, Rama
%A Aziz, Wilker
%A Shah, Kashif
%A Saz, Oscar
%A Hasan, Madina
%A AlHaribi, Ghada
%A Specia, Lucia
%A Hain, Thomas
%Y Federico, Marcello
%Y Stüker, Sebastian
%Y Yvon, François
%S Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign
%D 2014
%8 dec 4 5
%C Lake Tahoe, California
%F ng-etal-2014-usfd
%X The University of Sheffield (USFD) participated in the International Workshop for Spoken Language Translation (IWSLT) in 2014. In this paper, we will introduce the USFD SLT system for IWSLT. Automatic speech recognition (ASR) is achieved by two multi-pass deep neural network systems with adaptation and rescoring techniques. Machine translation (MT) is achieved by a phrase-based system. The USFD primary system incorporates state-of-the-art ASR and MT techniques and gives a BLEU score of 23.45 and 14.75 on the English-to-French and English-to-German speech-to-text translation task with the IWSLT 2014 data. The USFD contrastive systems explore the integration of ASR and MT by using a quality estimation system to rescore the ASR outputs, optimising towards better translation. This gives a further 0.54 and 0.26 BLEU improvement respectively on the IWSLT 2012 and 2014 evaluation data.
%U https://aclanthology.org/2014.iwslt-evaluation.11
%P 86-91
Markdown (Informal)
[The USFD SLT system for IWSLT 2014](https://aclanthology.org/2014.iwslt-evaluation.11) (Ng et al., IWSLT 2014)
ACL
- Raymond W. M. Ng, Mortaza Doulaty, Rama Doddipatla, Wilker Aziz, Kashif Shah, Oscar Saz, Madina Hasan, Ghada AlHaribi, Lucia Specia, and Thomas Hain. 2014. The USFD SLT system for IWSLT 2014. In Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign, pages 86–91, Lake Tahoe, California.