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    <abstract>This paper is related to the submission of the CFILT-IITB team for the task called IndicMT in WMT23. The paper describes our MT systems submitted to the WMT23 IndicMT shared task. The task focused on MT system development from/to English and four low-resource North-East Indian languages, viz., Assamese, Khasi, Manipuri, and Mizo. We trained them on a small parallel corpus resulting in poor-quality systems. Therefore, we utilize transfer learning with the help of a large pre-trained multilingual NMT system. Since this approach produced the best results, we submitted our NMT models for the shared task using this approach.</abstract>
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%0 Conference Proceedings
%T Machine Translation Advancements for Low-Resource Indian Languages in WMT23: CFILT-IITB‘s Effort for Bridging the Gap
%A Gaikwad, Pranav
%A Doshi, Meet
%A Deoghare, Sourabh
%A Bhattacharyya, Pushpak
%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 gaikwad-etal-2023-machine
%X This paper is related to the submission of the CFILT-IITB team for the task called IndicMT in WMT23. The paper describes our MT systems submitted to the WMT23 IndicMT shared task. The task focused on MT system development from/to English and four low-resource North-East Indian languages, viz., Assamese, Khasi, Manipuri, and Mizo. We trained them on a small parallel corpus resulting in poor-quality systems. Therefore, we utilize transfer learning with the help of a large pre-trained multilingual NMT system. Since this approach produced the best results, we submitted our NMT models for the shared task using this approach.
%R 10.18653/v1/2023.wmt-1.89
%U https://aclanthology.org/2023.wmt-1.89/
%U https://doi.org/10.18653/v1/2023.wmt-1.89
%P 950-953

Markdown (Informal)

[Machine Translation Advancements for Low-Resource Indian Languages in WMT23: CFILT-IITB’s Effort for Bridging the Gap](https://aclanthology.org/2023.wmt-1.89/) (Gaikwad et al., WMT 2023)

• Machine Translation Advancements for Low-Resource Indian Languages in WMT23: CFILT-IITB’s Effort for Bridging the Gap (Gaikwad et al., WMT 2023)

ACL

• Pranav Gaikwad, Meet Doshi, Sourabh Deoghare, and Pushpak Bhattacharyya. 2023. Machine Translation Advancements for Low-Resource Indian Languages in WMT23: CFILT-IITB’s Effort for Bridging the Gap. In Proceedings of the Eighth Conference on Machine Translation, pages 950–953, Singapore. Association for Computational Linguistics.