We analyze some of the fundamental design challenges that impact the
development of a multilingual state-of-the-art named entity transliteration
system, including curating bilingual named entity datasets and evaluation of
multiple transliteration methods. We empirically evaluate the transliteration
task using traditional weighted finite state transducer (WFST) approach against
two neural approaches: the encoder-decoder recurrent neural network method and
the recent, non-sequential Transformer method. In order to improve availability
of bilingual named entity transliteration datasets, we release personal name
bilingual dictionaries minded from Wikidata for English to Russian, Hebrew,
Arabic and Japanese Katakana. Our code and dictionaries are publicly available.

本文探讨了多语种最新命名实体转写系统的一些基本设计挑战，并使用传统的加权有限状态转换器方法对两种神经方法进行实证评估：编码器 - 解码器循环神经网络方法和最近的非顺序 Transformer 方法。为了提高双语命名实体转写数据集的可用性，我们在英语到俄语、希伯来语、阿拉伯语和日语片假名方面发布了从维基数据中构建的个人命名双语词典。我们的代码和词典已经公开发布。