@inproceedings{wang-etal-2014-nict,
title = "The {NICT} translation system for {IWSLT} 2014",
author = "Wang, Xiaolin and
Finch, Andrew and
Utiyama, Masao and
Watanabe, Taro and
Sumita, Eiichiro",
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.20",
pages = "139--142",
abstract = "This paper describes NICT{'}s participation in the IWSLT 2014 evaluation campaign for the TED Chinese-English translation shared-task. Our approach used a combination of phrase-based and hierarchical statistical machine translation (SMT) systems. Our focus was in several areas, specifically system combination, word alignment, and various language modeling techniques including the use of neural network joint models. Our experiments on the test set from the 2013 shared task, showed that an improvement in BLEU score can be gained in translation performance through all of these techniques, with the largest improvements coming from using large data sizes to train the language model.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-etal-2014-nict">
<titleInfo>
<title>The NICT translation system for IWSLT 2014</title>
</titleInfo>
<name type="personal">
<namePart type="given">Xiaolin</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andrew</namePart>
<namePart type="family">Finch</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Masao</namePart>
<namePart type="family">Utiyama</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Taro</namePart>
<namePart type="family">Watanabe</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Eiichiro</namePart>
<namePart type="family">Sumita</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2014-dec 4-5</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign</title>
</titleInfo>
<name type="personal">
<namePart type="given">Marcello</namePart>
<namePart type="family">Federico</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sebastian</namePart>
<namePart type="family">Stüker</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">François</namePart>
<namePart type="family">Yvon</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<place>
<placeTerm type="text">Lake Tahoe, California</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper describes NICT’s participation in the IWSLT 2014 evaluation campaign for the TED Chinese-English translation shared-task. Our approach used a combination of phrase-based and hierarchical statistical machine translation (SMT) systems. Our focus was in several areas, specifically system combination, word alignment, and various language modeling techniques including the use of neural network joint models. Our experiments on the test set from the 2013 shared task, showed that an improvement in BLEU score can be gained in translation performance through all of these techniques, with the largest improvements coming from using large data sizes to train the language model.</abstract>
<identifier type="citekey">wang-etal-2014-nict</identifier>
<location>
<url>https://aclanthology.org/2014.iwslt-evaluation.20</url>
</location>
<part>
<date>2014-dec 4-5</date>
<extent unit="page">
<start>139</start>
<end>142</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T The NICT translation system for IWSLT 2014
%A Wang, Xiaolin
%A Finch, Andrew
%A Utiyama, Masao
%A Watanabe, Taro
%A Sumita, Eiichiro
%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 wang-etal-2014-nict
%X This paper describes NICT’s participation in the IWSLT 2014 evaluation campaign for the TED Chinese-English translation shared-task. Our approach used a combination of phrase-based and hierarchical statistical machine translation (SMT) systems. Our focus was in several areas, specifically system combination, word alignment, and various language modeling techniques including the use of neural network joint models. Our experiments on the test set from the 2013 shared task, showed that an improvement in BLEU score can be gained in translation performance through all of these techniques, with the largest improvements coming from using large data sizes to train the language model.
%U https://aclanthology.org/2014.iwslt-evaluation.20
%P 139-142
Markdown (Informal)
[The NICT translation system for IWSLT 2014](https://aclanthology.org/2014.iwslt-evaluation.20) (Wang et al., IWSLT 2014)
ACL
- Xiaolin Wang, Andrew Finch, Masao Utiyama, Taro Watanabe, and Eiichiro Sumita. 2014. The NICT translation system for IWSLT 2014. In Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign, pages 139–142, Lake Tahoe, California.