Transliteration for Low-Resource Code-Switching Texts: Building an Automatic Cyrillic-to-Latin Converter for Tatar

Chihiro Taguchi, Yusuke Sakai, Taro Watanabe


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.
Anthology ID:
2021.calcs-1.18
Volume:
Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching
Month:
June
Year:
2021
Address:
Online
Venues:
CALCS | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
133–140
Language:
URL:
https://aclanthology.org/2021.calcs-1.18
DOI:
10.18653/v1/2021.calcs-1.18
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.calcs-1.18.pdf