Papers by Srinivas Gowriraj
DeMuX: Data-efficient Multilingual Learning (2024.naacl-long)
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| Challenge: | Pre-trained multilingual models have enabled deployment of NLP technologies for multiple languages, but their performance under an annotation budget remains an open question. |
| Approach: | They propose a framework that prescribes the exact data-points to label from vast amounts of unlabelled multilingual data, having unknown degrees of overlap with the target set. |
| Outcome: | The proposed framework outperforms strong baselines in 84% of the test cases in the zero-shot setting of disjoint source and target language sets. |