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        <title>MUNI-NLP Systems for Low-resource Indic Machine Translation</title>
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        <namePart type="given">Edoardo</namePart>
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            <title>Proceedings of the Eighth Conference on Machine Translation</title>
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            <namePart type="given">Philipp</namePart>
            <namePart type="family">Koehn</namePart>
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        <name type="personal">
            <namePart type="given">Tom</namePart>
            <namePart type="family">Kocmi</namePart>
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            <namePart type="given">Christof</namePart>
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    <abstract>The WMT 2023 Shared Task on Low-Resource Indic Language Translation featured to and from Assamese, Khasi, Manipuri, Mizo on one side and English on the other. We submitted systems supervised neural machine translation systems for each pair and direction and experimented with different configurations and settings for both preprocessing and training. Even if most of them did not reach competitive performance, our experiments uncovered some interesting points for further investigation, namely the relation between dataset and model size, and the impact of the training framework. Moreover, the results of some of our preliminary experiments on the use of word embeddings initialization, backtranslation, and model depth were in contrast with previous work. The final results also show some disagreement in the automated metrics employed in the evaluation.</abstract>
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%0 Conference Proceedings
%T MUNI-NLP Systems for Low-resource Indic Machine Translation
%A Signoroni, Edoardo
%A Rychly, Pavel
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F signoroni-rychly-2023-muni
%X The WMT 2023 Shared Task on Low-Resource Indic Language Translation featured to and from Assamese, Khasi, Manipuri, Mizo on one side and English on the other. We submitted systems supervised neural machine translation systems for each pair and direction and experimented with different configurations and settings for both preprocessing and training. Even if most of them did not reach competitive performance, our experiments uncovered some interesting points for further investigation, namely the relation between dataset and model size, and the impact of the training framework. Moreover, the results of some of our preliminary experiments on the use of word embeddings initialization, backtranslation, and model depth were in contrast with previous work. The final results also show some disagreement in the automated metrics employed in the evaluation.
%R 10.18653/v1/2023.wmt-1.91
%U https://aclanthology.org/2023.wmt-1.91
%U https://doi.org/10.18653/v1/2023.wmt-1.91
%P 959-966

Markdown (Informal)

[MUNI-NLP Systems for Low-resource Indic Machine Translation](https://aclanthology.org/2023.wmt-1.91) (Signoroni & Rychly, WMT 2023)

• MUNI-NLP Systems for Low-resource Indic Machine Translation (Signoroni & Rychly, WMT 2023)

ACL

• Edoardo Signoroni and Pavel Rychly. 2023. MUNI-NLP Systems for Low-resource Indic Machine Translation. In Proceedings of the Eighth Conference on Machine Translation, pages 959–966, Singapore. Association for Computational Linguistics.