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    <abstract>This paper describes the submission of the GUIT-NLP team in the “Shared Task: Low Resource Indic Language Translation” focusing on three low-resource language pairs: English-Mizo, English-Khasi, and English-Assamese. The initial phase involves an in-depth exploration of Neural Machine Translation (NMT) techniques tailored to the available data. Within this investigation, various Subword Tokenization approaches, model configurations (exploring differnt hyper-parameters etc.) of the general NMT pipeline are tested to identify the most effective method. Subsequently, we address the challenge of low-resource languages by leveraging monolingual data through an innovative and systematic application of the Back Translation technique for English-Mizo. During model training, the monolingual data is progressively integrated into the original bilingual dataset, with each iteration yielding higher-quality back translations. This iterative approach significantly enhances the model’s performance, resulting in a notable increase of +3.65 in BLEU scores. Further improvements of +5.59 are achieved through fine-tuning using authentic parallel data.</abstract>
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%0 Conference Proceedings
%T GUIT-NLP’s Submission to Shared Task: Low Resource Indic Language Translation
%A Ahmed, Mazida
%A Talukdar, Kuwali
%A Boruah, Parvez
%A Sarma, Prof. Shikhar Kumar
%A Kashyap, Kishore
%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 ahmed-etal-2023-guit
%X This paper describes the submission of the GUIT-NLP team in the “Shared Task: Low Resource Indic Language Translation” focusing on three low-resource language pairs: English-Mizo, English-Khasi, and English-Assamese. The initial phase involves an in-depth exploration of Neural Machine Translation (NMT) techniques tailored to the available data. Within this investigation, various Subword Tokenization approaches, model configurations (exploring differnt hyper-parameters etc.) of the general NMT pipeline are tested to identify the most effective method. Subsequently, we address the challenge of low-resource languages by leveraging monolingual data through an innovative and systematic application of the Back Translation technique for English-Mizo. During model training, the monolingual data is progressively integrated into the original bilingual dataset, with each iteration yielding higher-quality back translations. This iterative approach significantly enhances the model’s performance, resulting in a notable increase of +3.65 in BLEU scores. Further improvements of +5.59 are achieved through fine-tuning using authentic parallel data.
%R 10.18653/v1/2023.wmt-1.87
%U https://aclanthology.org/2023.wmt-1.87
%U https://doi.org/10.18653/v1/2023.wmt-1.87
%P 935-940

Markdown (Informal)

[GUIT-NLP’s Submission to Shared Task: Low Resource Indic Language Translation](https://aclanthology.org/2023.wmt-1.87) (Ahmed et al., WMT 2023)

• GUIT-NLP’s Submission to Shared Task: Low Resource Indic Language Translation (Ahmed et al., WMT 2023)

ACL

• Mazida Ahmed, Kuwali Talukdar, Parvez Boruah, Prof. Shikhar Kumar Sarma, and Kishore Kashyap. 2023. GUIT-NLP’s Submission to Shared Task: Low Resource Indic Language Translation. In Proceedings of the Eighth Conference on Machine Translation, pages 935–940, Singapore. Association for Computational Linguistics.