2024/10/04 by Pramit Sahoo, Sahoo, Pramit, Maharaj Brahma +3
Computer Science · #Parallel Computing and Optimization Techniques
paper · pdf · doi:10.48550/arxiv.2410.03215
In this paper, we describe our system for the WMT 24 shared task of Low-Resource Indic Language Translation. We consider eng ↔ 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 eng→as, eng→kha, eng→lus, eng→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.