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    <titleInfo>
        <title>Improving Neural Machine Translation for Sanskrit-English</title>
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    <name type="personal">
        <namePart type="given">Ravneet</namePart>
        <namePart type="family">Punia</namePart>
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        <namePart type="given">Aditya</namePart>
        <namePart type="family">Sharma</namePart>
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    <name type="personal">
        <namePart type="given">Sarthak</namePart>
        <namePart type="family">Pruthi</namePart>
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    <name type="personal">
        <namePart type="given">Minni</namePart>
        <namePart type="family">Jain</namePart>
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        <dateIssued>2020-12</dateIssued>
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            <title>Proceedings of the 17th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Pushpak</namePart>
            <namePart type="family">Bhattacharyya</namePart>
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            <namePart type="given">Dipti</namePart>
            <namePart type="given">Misra</namePart>
            <namePart type="family">Sharma</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Rajeev</namePart>
            <namePart type="family">Sangal</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <originInfo>
            <publisher>NLP Association of India (NLPAI)</publisher>
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                <placeTerm type="text">Indian Institute of Technology Patna, Patna, India</placeTerm>
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    <abstract>Sanskrit is one of the oldest languages of the Asian Subcontinent that fell out of common usage around 600 B.C. In this paper, we attempt to translate Sanskrit to English using Neural Machine Translation approaches based on Reinforcement Learning and Transfer learning that were never tried and tested on Sanskrit. Along with the paper, we also release monolingual Sanskrit and parallel aligned Sanskrit-English corpora for the research community. Our methodologies outperform the previous approaches applied to Sanskrit by various re- searchers and will further help the linguistic community to accelerate the costly and time consuming manual translation process.</abstract>
    <identifier type="citekey">punia-etal-2020-improving</identifier>
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        <url>https://aclanthology.org/2020.icon-main.30</url>
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        <date>2020-12</date>
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            <start>234</start>
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%0 Conference Proceedings
%T Improving Neural Machine Translation for Sanskrit-English
%A Punia, Ravneet
%A Sharma, Aditya
%A Pruthi, Sarthak
%A Jain, Minni
%Y Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Sangal, Rajeev
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F punia-etal-2020-improving
%X Sanskrit is one of the oldest languages of the Asian Subcontinent that fell out of common usage around 600 B.C. In this paper, we attempt to translate Sanskrit to English using Neural Machine Translation approaches based on Reinforcement Learning and Transfer learning that were never tried and tested on Sanskrit. Along with the paper, we also release monolingual Sanskrit and parallel aligned Sanskrit-English corpora for the research community. Our methodologies outperform the previous approaches applied to Sanskrit by various re- searchers and will further help the linguistic community to accelerate the costly and time consuming manual translation process.
%U https://aclanthology.org/2020.icon-main.30
%P 234-238

Markdown (Informal)

[Improving Neural Machine Translation for Sanskrit-English](https://aclanthology.org/2020.icon-main.30) (Punia et al., ICON 2020)

• Improving Neural Machine Translation for Sanskrit-English (Punia et al., ICON 2020)

ACL

• Ravneet Punia, Aditya Sharma, Sarthak Pruthi, and Minni Jain. 2020. Improving Neural Machine Translation for Sanskrit-English. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 234–238, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).