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

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.