{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nlip-lab-iith-low-resource-mt-system-for","title":"NLIP_Lab-IITH Low-Resource MT System for WMT24 Indic MT Shared Task","arxiv_id":"2410.03215","date":"2024-10-04","proceeding":null,"authors":["Pramit Sahoo","Maharaj Brahma","Maunendra Sankar Desarkar"],"abstract":"In this paper, we describe our system for the WMT 24 shared task of Low-Resource Indic Language Translation. We consider eng $\\leftrightarrow$ {as, kha, lus, mni} as participating language pairs. In this shared task, we explore the finetuning of a pre-trained model motivated by the pre-trained objective of aligning embeddings closer by alignment augmentation \\cite{lin-etal-2020-pre} for 22 scheduled Indian languages. Our primary system is based on language-specific finetuning on a pre-trained model. We achieve chrF2 scores of 50.6, 42.3, 54.9, and 66.3 on the official public test set for eng$\\rightarrow$as, eng$\\rightarrow$kha, eng$\\rightarrow$lus, eng$\\rightarrow$mni respectively. We also explore multilingual training with/without language grouping and layer-freezing. Our code, models, and generated translations are available here: https://github.com/pramitsahoo/WMT2024-LRILT.","url_abs":"https://arxiv.org/abs/2410.03215v1","url_pdf":"https://arxiv.org/pdf/2410.03215v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"nlip-lab-iith-low-resource-mt-system-for","repo_url":"https://github.com/pramitsahoo/wmt2024-lrilt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}