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    <titleInfo>
        <title>IITP-MT System for Gujarati-English News Translation Task at WMT 2019</title>
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    <name type="personal">
        <namePart type="given">Sukanta</namePart>
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    <name type="personal">
        <namePart type="given">Kamal</namePart>
        <namePart type="given">Kumar</namePart>
        <namePart type="family">Gupta</namePart>
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    <name type="personal">
        <namePart type="given">Asif</namePart>
        <namePart type="family">Ekbal</namePart>
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    <name type="personal">
        <namePart type="given">Pushpak</namePart>
        <namePart type="family">Bhattacharyya</namePart>
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            <title>Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)</title>
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    <abstract>We describe our submission to WMT 2019 News translation shared task for Gujarati-English language pair. We submit constrained systems, i.e, we rely on the data provided for this language pair and do not use any external data. We train Transformer based subword-level neural machine translation (NMT) system using original parallel corpus along with synthetic parallel corpus obtained through back-translation of monolingual data. Our primary systems achieve BLEU scores of 10.4 and 8.1 for Gujarati→English and English→Gujarati, respectively. We observe that incorporating monolingual data through back-translation improves the BLEU score significantly over baseline NMT and SMT systems for this language pair.</abstract>
    <identifier type="citekey">sen-etal-2019-iitp</identifier>
    <identifier type="doi">10.18653/v1/W19-5346</identifier>
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        <date>2019-08</date>
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            <start>407</start>
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%0 Conference Proceedings
%T IITP-MT System for Gujarati-English News Translation Task at WMT 2019
%A Sen, Sukanta
%A Gupta, Kamal Kumar
%A Ekbal, Asif
%A Bhattacharyya, Pushpak
%S Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F sen-etal-2019-iitp
%X We describe our submission to WMT 2019 News translation shared task for Gujarati-English language pair. We submit constrained systems, i.e, we rely on the data provided for this language pair and do not use any external data. We train Transformer based subword-level neural machine translation (NMT) system using original parallel corpus along with synthetic parallel corpus obtained through back-translation of monolingual data. Our primary systems achieve BLEU scores of 10.4 and 8.1 for Gujarati→English and English→Gujarati, respectively. We observe that incorporating monolingual data through back-translation improves the BLEU score significantly over baseline NMT and SMT systems for this language pair.
%R 10.18653/v1/W19-5346
%U https://aclanthology.org/W19-5346
%U https://doi.org/10.18653/v1/W19-5346
%P 407-411

Markdown (Informal)

[IITP-MT System for Gujarati-English News Translation Task at WMT 2019](https://aclanthology.org/W19-5346) (Sen et al., WMT 2019)

• IITP-MT System for Gujarati-English News Translation Task at WMT 2019 (Sen et al., WMT 2019)

ACL

• Sukanta Sen, Kamal Kumar Gupta, Asif Ekbal, and Pushpak Bhattacharyya. 2019. IITP-MT System for Gujarati-English News Translation Task at WMT 2019. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 407–411, Florence, Italy. Association for Computational Linguistics.