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        <title>MTNLP-IIITH: Machine Translation for Low-Resource Indic Languages</title>
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        <namePart type="given">Abhinav</namePart>
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            <title>Proceedings of the Ninth Conference on Machine Translation</title>
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    <abstract>Machine Translation for low-resource languages presents significant challenges, primarily due to limited data availability. We have a baseline model and a primary model. For the baseline model, we first fine-tune the mBART model (mbart-large-50-many-to-many-mmt) for the language pairs English-Khasi, Khasi-English, English-Manipuri, and Manipuri-English. We then augment the dataset by back-translating from Indic languages to English. To enhance data quality, we fine-tune the LaBSE model specifically for Khasi and Manipuri, generating sentence embeddings and applying a cosine similarity threshold of 0.84 to filter out low-quality back-translations. The filtered data is combined with the original training data and used to further fine-tune the mBART model, creating our primary model. The results show that the primary model slightly outperforms the baseline model, with the best performance achieved by the English-to-Khasi (en-kh) primary model, which recorded a BLEU score of 0.0492, a chrF score of 0.3316, and a METEOR score of 0.2589 (on a scale of 0 to 1), with similar results for other language pairs.</abstract>
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%0 Conference Proceedings
%T MTNLP-IIITH: Machine Translation for Low-Resource Indic Languages
%A P M, Abhinav
%A Shetye, Ketaki
%A Krishnamurthy, Parameswari
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F p-m-etal-2024-mtnlp
%X Machine Translation for low-resource languages presents significant challenges, primarily due to limited data availability. We have a baseline model and a primary model. For the baseline model, we first fine-tune the mBART model (mbart-large-50-many-to-many-mmt) for the language pairs English-Khasi, Khasi-English, English-Manipuri, and Manipuri-English. We then augment the dataset by back-translating from Indic languages to English. To enhance data quality, we fine-tune the LaBSE model specifically for Khasi and Manipuri, generating sentence embeddings and applying a cosine similarity threshold of 0.84 to filter out low-quality back-translations. The filtered data is combined with the original training data and used to further fine-tune the mBART model, creating our primary model. The results show that the primary model slightly outperforms the baseline model, with the best performance achieved by the English-to-Khasi (en-kh) primary model, which recorded a BLEU score of 0.0492, a chrF score of 0.3316, and a METEOR score of 0.2589 (on a scale of 0 to 1), with similar results for other language pairs.
%U https://aclanthology.org/2024.wmt-1.65
%P 751-755

Markdown (Informal)

[MTNLP-IIITH: Machine Translation for Low-Resource Indic Languages](https://aclanthology.org/2024.wmt-1.65) (P M et al., WMT 2024)

• MTNLP-IIITH: Machine Translation for Low-Resource Indic Languages (P M et al., WMT 2024)

ACL

• Abhinav P M, Ketaki Shetye, and Parameswari Krishnamurthy. 2024. MTNLP-IIITH: Machine Translation for Low-Resource Indic Languages. In Proceedings of the Ninth Conference on Machine Translation, pages 751–755, Miami, Florida, USA. Association for Computational Linguistics.