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        <title>A Sanskrit grammar-based approach to identify and address gaps in Google Translate’s Sanskrit-English zero-shot NMT</title>
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        <namePart type="given">Amit</namePart>
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            <title>Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD)</title>
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            <namePart type="given">Ellen</namePart>
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            <namePart type="given">Shalom</namePart>
            <namePart type="family">Lappin</namePart>
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            <namePart type="given">Sharid</namePart>
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    <abstract>In this work, we test the working of Google Translate’s recently introduced Sanskrit-English translation system using a relatively small set of probe test cases designed to focus on those areas that we expect, based on a knowledge of Sanskrit and English grammar, to pose a challenge for translation between Sanskrit and English. We summarize the findings that point to significant gaps in the current Zero-Shot Neural Multilingual Translation (Zero-Shot NMT) approach to Sanskrit-English translation. We then suggest an approach based on Sanskrit grammar to create a differential parallel corpus as a corrective training data to address such gaps. This approach should also generalize to other pairs of languages that have low availability of learning resources, but a good grammar theory.</abstract>
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%0 Conference Proceedings
%T A Sanskrit grammar-based approach to identify and address gaps in Google Translate’s Sanskrit-English zero-shot NMT
%A Rao, Amit
%A Gopinath, Kanchi
%Y Breitholtz, Ellen
%Y Lappin, Shalom
%Y Loaiciga, Sharid
%Y Ilinykh, Nikolai
%Y Dobnik, Simon
%S Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD)
%D 2023
%8 September
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F rao-gopinath-2023-sanskrit
%X In this work, we test the working of Google Translate’s recently introduced Sanskrit-English translation system using a relatively small set of probe test cases designed to focus on those areas that we expect, based on a knowledge of Sanskrit and English grammar, to pose a challenge for translation between Sanskrit and English. We summarize the findings that point to significant gaps in the current Zero-Shot Neural Multilingual Translation (Zero-Shot NMT) approach to Sanskrit-English translation. We then suggest an approach based on Sanskrit grammar to create a differential parallel corpus as a corrective training data to address such gaps. This approach should also generalize to other pairs of languages that have low availability of learning resources, but a good grammar theory.
%U https://aclanthology.org/2023.clasp-1.16
%P 141-166

Markdown (Informal)

[A Sanskrit grammar-based approach to identify and address gaps in Google Translate’s Sanskrit-English zero-shot NMT](https://aclanthology.org/2023.clasp-1.16) (Rao & Gopinath, CLASP 2023)

• A Sanskrit grammar-based approach to identify and address gaps in Google Translate’s Sanskrit-English zero-shot NMT (Rao & Gopinath, CLASP 2023)

ACL

• Amit Rao and Kanchi Gopinath. 2023. A Sanskrit grammar-based approach to identify and address gaps in Google Translate’s Sanskrit-English zero-shot NMT. In Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD), pages 141–166, Gothenburg, Sweden. Association for Computational Linguistics.