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Optimizing Transformer for Low-Resource Neural Machine Translation

2020-11-04 13:12:29
Ali Araabi, Christof Monz

Abstract

Language pairs with limited amounts of parallel data, also known as low-resource languages, remain a challenge for neural machine translation. While the Transformer model has achieved significant improvements for many language pairs and has become the de facto mainstream architecture, its capability under low-resource conditions has not been fully investigated yet. Our experiments on different subsets of the IWSLT14 training data show that the effectiveness of Transformer under low-resource conditions is highly dependent on the hyper-parameter settings. Our experiments show that using an optimized Transformer for low-resource conditions improves the translation quality up to 7.3 BLEU points compared to using the Transformer default settings.

Abstract (translated)

URL

https://arxiv.org/abs/2011.02266

PDF

https://arxiv.org/pdf/2011.02266.pdf


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