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How Far Can 100 Samples Go? Unlocking Overall Zero-Shot Multilingual Translation via Tiny Multi-Parallel Data
Feb. 28, 2024, 5:44 a.m. | Di Wu, Shaomu Tan, Yan Meng, David Stap, Christof Monz
cs.LG updates on arXiv.org arxiv.org
Abstract: Zero-shot translation aims to translate between language pairs not seen during training in Multilingual Machine Translation (MMT) and is largely considered an open problem. A common, albeit resource-consuming, solution is to add as many related translation directions as possible to the training corpus. In this paper, we show that for an English-centric model, surprisingly large zero-shot improvements can be achieved by simply fine-tuning with a very small amount of multi-parallel data. For example, on the …
abstract arxiv cs.cl cs.lg data language machine machine translation multilingual samples solution training translate translation type via zero-shot
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