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        <title>Impacts of Approaches for Agglutinative-LRL Neural Machine Translation (NMT): A Case Study on Manipuri-English Pair</title>
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        <namePart type="given">Moirangthem</namePart>
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        <namePart type="given">Nongbri</namePart>
        <namePart type="family">Lavinia</namePart>
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    <name type="personal">
        <namePart type="given">Salam</namePart>
        <namePart type="family">Samarendra Singh</namePart>
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    <name type="personal">
        <namePart type="given">Nongmeikapam</namePart>
        <namePart type="family">Kishorjit</namePart>
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            <title>Proceedings of the 20th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="family">Jyoti</namePart>
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            <namePart type="given">Devi</namePart>
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    <abstract>Neural Machine Translation (NMT) is known to be extremely challenging for Low-Resource Languages (LRL) with complex morphology. This work deals with the NMT of a specific LRL called Manipuri/Meeteilon, which is a highly agglutinative language where words have extensive suffixation with limited prefixation. The work studies and discusses the impacts of approaches to mitigate the issues of NMT involving agglutinative LRL in a strictly low-resource setting. The research work experimented with several methods and techniques including subword tokenization, tuning of the selfattention-based NMT model, utilization of monolingual corpus by iterative backtranslation, embedding-based sentence filtering for back translation. This research work in the strictly low resource setting of only 21204 training sentences showed remarkable results with a BLEU score of 28.17 for Manipuri to English translation.</abstract>
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%0 Conference Proceedings
%T Impacts of Approaches for Agglutinative-LRL Neural Machine Translation (NMT): A Case Study on Manipuri-English Pair
%A Gourashyam, Moirangthem
%A Lavinia, Nongbri
%A Samarendra Singh, Salam
%A Kishorjit, Nongmeikapam
%Y Jyoti, D. Pawar
%Y Sobha, Lalitha Devi
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F gourashyam-etal-2023-impacts
%X Neural Machine Translation (NMT) is known to be extremely challenging for Low-Resource Languages (LRL) with complex morphology. This work deals with the NMT of a specific LRL called Manipuri/Meeteilon, which is a highly agglutinative language where words have extensive suffixation with limited prefixation. The work studies and discusses the impacts of approaches to mitigate the issues of NMT involving agglutinative LRL in a strictly low-resource setting. The research work experimented with several methods and techniques including subword tokenization, tuning of the selfattention-based NMT model, utilization of monolingual corpus by iterative backtranslation, embedding-based sentence filtering for back translation. This research work in the strictly low resource setting of only 21204 training sentences showed remarkable results with a BLEU score of 28.17 for Manipuri to English translation.
%U https://aclanthology.org/2023.icon-1.19
%P 191-201

Markdown (Informal)

[Impacts of Approaches for Agglutinative-LRL Neural Machine Translation (NMT): A Case Study on Manipuri-English Pair](https://aclanthology.org/2023.icon-1.19) (Gourashyam et al., ICON 2023)

• Impacts of Approaches for Agglutinative-LRL Neural Machine Translation (NMT): A Case Study on Manipuri-English Pair (Gourashyam et al., ICON 2023)

ACL

• Moirangthem Gourashyam, Nongbri Lavinia, Salam Samarendra Singh, and Nongmeikapam Kishorjit. 2023. Impacts of Approaches for Agglutinative-LRL Neural Machine Translation (NMT): A Case Study on Manipuri-English Pair. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 191–201, Goa University, Goa, India. NLP Association of India (NLPAI).