论文标题
探索印度语言的配对NMT
Exploring Pair-Wise NMT for Indian Languages
论文作者
论文摘要
在本文中，我们解决了改善对特定低资源印度语言的配对机器翻译的任务。多语言NMT模型已经证明了对资源贫乏语言的合理有效性。在这项工作中，我们表明，通过通过过滤后的反翻译过程以及随后对有限的配对语言语料库进行微调，可以通过反向翻译来显着改善这些模型的性能。本文中的分析表明，这种方法可以显着改善多语种模型的性能，从而为各种印度语言带来最新的结果。
In this paper, we address the task of improving pair-wise machine translation for specific low resource Indian languages. Multilingual NMT models have demonstrated a reasonable amount of effectiveness on resource-poor languages. In this work, we show that the performance of these models can be significantly improved upon by using back-translation through a filtered back-translation process and subsequent fine-tuning on the limited pair-wise language corpora. The analysis in this paper suggests that this method can significantly improve a multilingual model's performance over its baseline, yielding state-of-the-art results for various Indian languages.
