论文标题
优化低资源神经机器翻译的变压器
Optimizing Transformer for Low-Resource Neural Machine Translation
论文作者
论文摘要
语言对具有有限的平行数据，也称为低资源语言，仍然是神经机器翻译的挑战。尽管变压器模型已经为许多语言对取得了重大改进，并且已成为事实上的主流体系结构，但其在低资源条件下的能力尚未得到充分研究。我们对IWSLT14培训数据不同子集的实验表明，在低资源条件下变压器的有效性高度取决于高参数设置。我们的实验表明，与使用Transformer默认设置相比，使用优化的变压器在低资源条件下可提高转换质量高达7.3个BLEU点。
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.
