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        <title>Coupling Statistical Machine Translation with Rule-based Transfer and Generation</title>
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        <namePart type="given">Arafat</namePart>
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        <namePart type="given">Prasanth</namePart>
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        <namePart type="given">Sudheer</namePart>
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            <title>Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers</title>
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    <abstract>In this paper, we present the insights gained from a detailed study of coupling a highly modular English-Hindi RBMT system with a standard phrase-based SMT system. Coupling the RBMT and SMT systems at various stages in the RBMT pipeline, we observe the effects of the source transformations at each stage on the performance of the coupled MT system. We propose an architecture that systematically exploits the structural transfer and robust generation capabilities of the RBMT system. Working with the English-Hindi language pair, we show that the coupling configurations explored in our experiments help address different aspects of the typological divergence between these languages. In spite of working with very small datasets, we report significant improvements both in terms of BLEU (7.14 and 0.87 over the RBMT and the SMT baselines respectively) and subjective evaluation (relative decrease of 17% in SSER).</abstract>
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%0 Conference Proceedings
%T Coupling Statistical Machine Translation with Rule-based Transfer and Generation
%A Ahsan, Arafat
%A Kolachina, Prasanth
%A Kolachina, Sudheer
%A Misra, Dipti
%A Sangal, Rajeev
%S Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2010
%8 oct 31 nov 4
%I Association for Machine Translation in the Americas
%C Denver, Colorado, USA
%F ahsan-etal-2010-coupling
%X In this paper, we present the insights gained from a detailed study of coupling a highly modular English-Hindi RBMT system with a standard phrase-based SMT system. Coupling the RBMT and SMT systems at various stages in the RBMT pipeline, we observe the effects of the source transformations at each stage on the performance of the coupled MT system. We propose an architecture that systematically exploits the structural transfer and robust generation capabilities of the RBMT system. Working with the English-Hindi language pair, we show that the coupling configurations explored in our experiments help address different aspects of the typological divergence between these languages. In spite of working with very small datasets, we report significant improvements both in terms of BLEU (7.14 and 0.87 over the RBMT and the SMT baselines respectively) and subjective evaluation (relative decrease of 17% in SSER).
%U https://aclanthology.org/2010.amta-papers.6

Markdown (Informal)

[Coupling Statistical Machine Translation with Rule-based Transfer and Generation](https://aclanthology.org/2010.amta-papers.6) (Ahsan et al., AMTA 2010)

• Coupling Statistical Machine Translation with Rule-based Transfer and Generation (Ahsan et al., AMTA 2010)

ACL

• Arafat Ahsan, Prasanth Kolachina, Sudheer Kolachina, Dipti Misra, and Rajeev Sangal. 2010. Coupling Statistical Machine Translation with Rule-based Transfer and Generation. In Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers, Denver, Colorado, USA. Association for Machine Translation in the Americas.