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- W3094981855 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." @default.
- W3094981855 created "2020-11-09" @default.
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- W3094981855 date "2020-11-04" @default.
- W3094981855 modified "2023-09-26" @default.
- W3094981855 title "Optimizing Transformer for Low-Resource Neural Machine Translation" @default.
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