@inproceedings{sankaran-sarkar-2014-bayesian,
title = "{B}ayesian iterative-cascade framework for hierarchical phrase-based translation",
author = "Sankaran, Baskaran and
Sarkar, Anoop",
editor = "Al-Onaizan, Yaser and
Simard, Michel",
booktitle = "Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track",
month = oct # " 22-26",
year = "2014",
address = "Vancouver, Canada",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2014.amta-researchers.2",
pages = "15--27",
abstract = "The typical training of a hierarchical phrase-based machine translation involves a pipeline of multiple steps where mistakes in early steps of the pipeline are propagated without any scope for rectifying them. Additionally the alignments are trained independent of and without being informed of the end goal and hence are not optimized for translation. We introduce a novel Bayesian iterative-cascade framework for training Hiero-style model that learns the alignments together with the synchronous translation grammar in an iterative setting. Our framework addresses the above mentioned issues and provides an elegant and principled alternative to the existing training pipeline. Based on the validation experiments involving two language pairs, our proposed iterative-cascade framework shows consistent gains over the traditional training pipeline for hierarchical translation.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sankaran-sarkar-2014-bayesian">
<titleInfo>
<title>Bayesian iterative-cascade framework for hierarchical phrase-based translation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Baskaran</namePart>
<namePart type="family">Sankaran</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anoop</namePart>
<namePart type="family">Sarkar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2014-oct 22-26</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yaser</namePart>
<namePart type="family">Al-Onaizan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Michel</namePart>
<namePart type="family">Simard</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Machine Translation in the Americas</publisher>
<place>
<placeTerm type="text">Vancouver, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The typical training of a hierarchical phrase-based machine translation involves a pipeline of multiple steps where mistakes in early steps of the pipeline are propagated without any scope for rectifying them. Additionally the alignments are trained independent of and without being informed of the end goal and hence are not optimized for translation. We introduce a novel Bayesian iterative-cascade framework for training Hiero-style model that learns the alignments together with the synchronous translation grammar in an iterative setting. Our framework addresses the above mentioned issues and provides an elegant and principled alternative to the existing training pipeline. Based on the validation experiments involving two language pairs, our proposed iterative-cascade framework shows consistent gains over the traditional training pipeline for hierarchical translation.</abstract>
<identifier type="citekey">sankaran-sarkar-2014-bayesian</identifier>
<location>
<url>https://aclanthology.org/2014.amta-researchers.2</url>
</location>
<part>
<date>2014-oct 22-26</date>
<extent unit="page">
<start>15</start>
<end>27</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Bayesian iterative-cascade framework for hierarchical phrase-based translation
%A Sankaran, Baskaran
%A Sarkar, Anoop
%Y Al-Onaizan, Yaser
%Y Simard, Michel
%S Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track
%D 2014
%8 oct 22 26
%I Association for Machine Translation in the Americas
%C Vancouver, Canada
%F sankaran-sarkar-2014-bayesian
%X The typical training of a hierarchical phrase-based machine translation involves a pipeline of multiple steps where mistakes in early steps of the pipeline are propagated without any scope for rectifying them. Additionally the alignments are trained independent of and without being informed of the end goal and hence are not optimized for translation. We introduce a novel Bayesian iterative-cascade framework for training Hiero-style model that learns the alignments together with the synchronous translation grammar in an iterative setting. Our framework addresses the above mentioned issues and provides an elegant and principled alternative to the existing training pipeline. Based on the validation experiments involving two language pairs, our proposed iterative-cascade framework shows consistent gains over the traditional training pipeline for hierarchical translation.
%U https://aclanthology.org/2014.amta-researchers.2
%P 15-27
Markdown (Informal)
[Bayesian iterative-cascade framework for hierarchical phrase-based translation](https://aclanthology.org/2014.amta-researchers.2) (Sankaran & Sarkar, AMTA 2014)
ACL