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        <title>Extending a probabilistic phrase alignment approach for SMT</title>
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        <namePart type="given">Mridul</namePart>
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        <namePart type="given">Sanjika</namePart>
        <namePart type="family">Hewavitharana</namePart>
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            <title>Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign</title>
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            <namePart type="given">Marcello</namePart>
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            <namePart type="given">Mei-Yuh</namePart>
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    <abstract>Phrase alignment is a crucial step in phrase-based statistical machine translation. We explore a way of improving phrase alignment by adding syntactic information in the form of chunks as soft constraints guided by an in-depth and detailed analysis on a hand-aligned data set. We extend a probabilistic phrase alignment model that extracts phrase pairs by optimizing phrase pair boundaries over the sentence pair [1]. The boundaries of the target phrase are chosen such that the overall sentence alignment probability is optimal. Viterbi alignment information is also added in the extended model with a view of improving phrase alignment. We extract phrase pairs using a relatively larger number of features which are discriminatively trained using a large-margin online learning algorithm, i.e., Margin Infused Relaxed Algorithm (MIRA) and integrate it in our approach. Initial experiments show improvements in both phrase alignment and translation quality for Arabic-English on a moderate-size translation task.</abstract>
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%0 Conference Proceedings
%T Extending a probabilistic phrase alignment approach for SMT
%A Gupta, Mridul
%A Hewavitharana, Sanjika
%A Vogel, Stephan
%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 gupta-etal-2011-extending
%X Phrase alignment is a crucial step in phrase-based statistical machine translation. We explore a way of improving phrase alignment by adding syntactic information in the form of chunks as soft constraints guided by an in-depth and detailed analysis on a hand-aligned data set. We extend a probabilistic phrase alignment model that extracts phrase pairs by optimizing phrase pair boundaries over the sentence pair [1]. The boundaries of the target phrase are chosen such that the overall sentence alignment probability is optimal. Viterbi alignment information is also added in the extended model with a view of improving phrase alignment. We extract phrase pairs using a relatively larger number of features which are discriminatively trained using a large-margin online learning algorithm, i.e., Margin Infused Relaxed Algorithm (MIRA) and integrate it in our approach. Initial experiments show improvements in both phrase alignment and translation quality for Arabic-English on a moderate-size translation task.
%U https://aclanthology.org/2011.iwslt-evaluation.23
%P 175-182

Markdown (Informal)

[Extending a probabilistic phrase alignment approach for SMT](https://aclanthology.org/2011.iwslt-evaluation.23) (Gupta et al., IWSLT 2011)

• Extending a probabilistic phrase alignment approach for SMT (Gupta et al., IWSLT 2011)

ACL

• Mridul Gupta, Sanjika Hewavitharana, and Stephan Vogel. 2011. Extending a probabilistic phrase alignment approach for SMT. In Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign, pages 175–182, San Francisco, California.