A Comparative Study of Extremely Low-Resource Transliteration of the World's Languages

被引：0

作者：

Wu, Winston ^{[1
]}

Yarowsky, David ^{[1
]}

机构：

[1] Johns Hopkins Univ, Ctr Language & Speech Proc, Dept Comp Sci, Baltimore, MD 21218 USA

来源：

PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2018) | 2018年

关键词：

Bible; alignment; named entities; translation; transliteration;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Transliteration from low-resource languages is difficult, in large part due to the small amounts of data available for training transliteration systems. In this paper, we evaluate the effectiveness of several translation methods in the task of transliterating around 1000 Bible names from 591 languages into English. In this extremely low-resource task, we found that a phrase-based MT system performs much better than other methods, including a g2p system and a neural MT system. However, by combining the data and training a single neural system, we discovered significant gains over single-language systems. We release the output from each system for comparative analysis.

引用

页码：938 / 943

页数：6

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