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        <title>A probabilistic feature-based fill-up for SMT</title>
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        <namePart type="given">Liangyou</namePart>
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        <dateIssued>2014-oct&quot; 22-26&quot;</dateIssued>
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            <title>Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track</title>
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    <abstract>In this paper, we describe an effective translation model combination approach based on the estimation of a probabilistic Support Vector Machine (SVM). We collect domain knowledge from both in-domain and general-domain corpora inspired by a commonly used data selection algorithm, which we then use as features for the SVM training. Drawing on previous work on binary-featured phrase table fill-up (Nakov, 2008; Bisazza et al., 2011), we substitute the binary feature in the original work with our probabilistic domain-likeness feature. Later, we design two experiments to evaluate the proposed probabilistic feature-based approach on the French-to-English language pair using data provided at WMT07, WMT13 and IWLST11 translation tasks. Our experiments demonstrate that translation performance can gain significant improvements of up to +0.36 and +0.82 BLEU scores by using our probabilistic feature-based translation model fill-up approach compared with the binary featured fill-up approach in both experiments.</abstract>
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%0 Conference Proceedings
%T A probabilistic feature-based fill-up for SMT
%A Zhang, Jian
%A Li, Liangyou
%A Way, Andy
%A Liu, Qun
%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 zhang-etal-2014-probabilistic
%X In this paper, we describe an effective translation model combination approach based on the estimation of a probabilistic Support Vector Machine (SVM). We collect domain knowledge from both in-domain and general-domain corpora inspired by a commonly used data selection algorithm, which we then use as features for the SVM training. Drawing on previous work on binary-featured phrase table fill-up (Nakov, 2008; Bisazza et al., 2011), we substitute the binary feature in the original work with our probabilistic domain-likeness feature. Later, we design two experiments to evaluate the proposed probabilistic feature-based approach on the French-to-English language pair using data provided at WMT07, WMT13 and IWLST11 translation tasks. Our experiments demonstrate that translation performance can gain significant improvements of up to +0.36 and +0.82 BLEU scores by using our probabilistic feature-based translation model fill-up approach compared with the binary featured fill-up approach in both experiments.
%U https://aclanthology.org/2014.amta-researchers.8
%P 96-109

Markdown (Informal)

[A probabilistic feature-based fill-up for SMT](https://aclanthology.org/2014.amta-researchers.8) (Zhang et al., AMTA 2014)

• A probabilistic feature-based fill-up for SMT (Zhang et al., AMTA 2014)

ACL

• Jian Zhang, Liangyou Li, Andy Way, and Qun Liu. 2014. A probabilistic feature-based fill-up for SMT. In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track, pages 96–109, Vancouver, Canada. Association for Machine Translation in the Americas.