Paper Info
Title
Transliteration for Low-Resource Code-Switching Texts: Building an Automatic Cyrillic-to-Latin Converter for Tatar.
Abstract
We introduce a Cyrillic-to-Latin transliterator for the Tatar language based on subword-level language identification. The transliteration is a challenging task due to the following two reasons. First, because modern Tatar texts often contain intra-word code-switching to Russian, a different transliteration set of rules needs to be applied to each morpheme depending on the language, which necessitates morpheme-level language identification. Second, the fact that Tatar is a low-resource language, with most of the texts in Cyrillic, makes it difficult to prepare a sufficient dataset. Given this situation, we proposed a transliteration method based on subword-level language identification. We trained a language classifier with monolingual Tatar and Russian texts, and applied different transliteration rules in accord with the identified language. The results demonstrate that our proposed method outscores other Tatar transliteration tools, and imply that it correctly transcribes Russian loanwords to some extent.
Year
DOI
Venue
2021
10.18653/v1/2021.calcs-1.18
Workshop on Computational Approaches to Linguistic Code-Switching (CALCS)
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Chihiro Taguchi100.34
Yusuke Sakai211.03
Taro Watanabe3123.33