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    <titleInfo>
        <title>DesiPayanam: developing an Indic travel partner</title>
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    <name type="personal">
        <namePart type="given">Diviya</namePart>
        <namePart type="family">K N</namePart>
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    <name type="personal">
        <namePart type="given">Mrinalini</namePart>
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    <name type="personal">
        <namePart type="given">Vijayalakshmi</namePart>
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    <name type="personal">
        <namePart type="given">Thenmozhi</namePart>
        <namePart type="family">J</namePart>
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    <name type="personal">
        <namePart type="given">Nagarajan</namePart>
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            <title>Proceedings of the 21st International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Sobha</namePart>
            <namePart type="family">Lalitha Devi</namePart>
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        <name type="personal">
            <namePart type="given">Karunesh</namePart>
            <namePart type="family">Arora</namePart>
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    <abstract>Domain-specific machine translation (MT) systems are essential in bridging the communication gap between people across different businesses, economies, and countries. India, a linguistically rich country with a booming tourism industry is a perfect market for such an MT system. On this note, the current work aims to develop a domain-specific transformer-based MT system for Hindi-to-Tamil translation. In the current work, neural-based MT (NMT) model is trained from scratch and the hyper-parameters of the model architecture are modified to analyze its effect on the translation performance. Further, a finetuning approach is adopted to finetune a pretrained transformer MT model to better suit the tourism domain. The proposed experiments are observed to improve the BLEU scores of the translation system by a maximum of 1% and 4% for the training from scratch and finetuned systems respectively.</abstract>
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%0 Conference Proceedings
%T DesiPayanam: developing an Indic travel partner
%A K N, Diviya
%A K, Mrinalini
%A P, Vijayalakshmi
%A J, Thenmozhi
%A T, Nagarajan
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F k-n-etal-2024-desipayanam
%X Domain-specific machine translation (MT) systems are essential in bridging the communication gap between people across different businesses, economies, and countries. India, a linguistically rich country with a booming tourism industry is a perfect market for such an MT system. On this note, the current work aims to develop a domain-specific transformer-based MT system for Hindi-to-Tamil translation. In the current work, neural-based MT (NMT) model is trained from scratch and the hyper-parameters of the model architecture are modified to analyze its effect on the translation performance. Further, a finetuning approach is adopted to finetune a pretrained transformer MT model to better suit the tourism domain. The proposed experiments are observed to improve the BLEU scores of the translation system by a maximum of 1% and 4% for the training from scratch and finetuned systems respectively.
%U https://aclanthology.org/2024.icon-1.56/
%P 480-484

Markdown (Informal)

[DesiPayanam: developing an Indic travel partner](https://aclanthology.org/2024.icon-1.56/) (K N et al., ICON 2024)

• DesiPayanam: developing an Indic travel partner (K N et al., ICON 2024)

ACL

• Diviya K N, Mrinalini K, Vijayalakshmi P, Thenmozhi J, and Nagarajan T. 2024. DesiPayanam: developing an Indic travel partner. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 480–484, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).