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    <titleInfo>
        <title>Quality Estimation of Machine Translated Texts based on Direct Evidence Approach</title>
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        <namePart type="given">Vibhuti</namePart>
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        <namePart type="given">Narayana</namePart>
        <namePart type="given">Murthy</namePart>
        <namePart type="family">Kavi</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>Quality Estimation task deals with the estimation of quality of translations produced by a Machine Translation system without depending on Reference Translations. A number of approaches have been suggested over the years. In this paper we show that the parallel corpus used as training data for training the MT system holds direct clues for estimating the quality of translations produced by the MT system. Our experiments show that this simple, direct and computationally efficient method holds promise for quality estimation of translations produced by any purely data driven machine translation system.</abstract>
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%0 Conference Proceedings
%T Quality Estimation of Machine Translated Texts based on Direct Evidence Approach
%A Kumari, Vibhuti
%A Kavi, Narayana Murthy
%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 kumari-kavi-2024-quality
%X Quality Estimation task deals with the estimation of quality of translations produced by a Machine Translation system without depending on Reference Translations. A number of approaches have been suggested over the years. In this paper we show that the parallel corpus used as training data for training the MT system holds direct clues for estimating the quality of translations produced by the MT system. Our experiments show that this simple, direct and computationally efficient method holds promise for quality estimation of translations produced by any purely data driven machine translation system.
%U https://aclanthology.org/2024.icon-1.16/
%P 139-148

Markdown (Informal)

[Quality Estimation of Machine Translated Texts based on Direct Evidence Approach](https://aclanthology.org/2024.icon-1.16/) (Kumari & Kavi, ICON 2024)

• Quality Estimation of Machine Translated Texts based on Direct Evidence Approach (Kumari & Kavi, ICON 2024)

ACL

• Vibhuti Kumari and Narayana Murthy Kavi. 2024. Quality Estimation of Machine Translated Texts based on Direct Evidence Approach. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 139–148, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).