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NLIP_Lab-IITH Low-Resource MT System for WMT24 Indic MT Shared Task

arXiv 2410.03215 ↗First posted 4 Oct 2024cs.CL cs.AIcited by 0

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Abstract (arXiv)

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 citelin-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 engrightarrowas, engrightarrowkha, engrightarrowlus, engrightarrowmni 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.

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References · 12

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  10. 2022Tencent's Multilingual Machine Translation System for WMT22 Large-Scale African Languagescited by 13
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