So Miyagawa - Ainu–Japanese Bi-directional Neural Machine Translation: A Step Towards Linguistic Preservation of Ainu, An Under-Resourced Indigenous Language in Japan

jdmdh:13151 - Journal of Data Mining & Digital Humanities, April 29, 2024, NLP4DH - https://doi.org/10.46298/jdmdh.13151
Ainu–Japanese Bi-directional Neural Machine Translation: A Step Towards Linguistic Preservation of Ainu, An Under-Resourced Indigenous Language in JapanArticle

Authors: Miyagawa, So ORCID1

  • 1 National Institute for Japanese Language and Linguistics

This study presents a groundbreaking approach to preserving the Ainu language, recognized as critically endangered by UNESCO, by developing a bi-directional neural machine translation (MT) system between Ainu and Japanese. Utilizing the Marian MT framework, known for its effectiveness with resource-scarce languages, the research aims to overcome the linguistic complexities inherent in Ainu's polysynthetic structure. The paper delineates a comprehensive methodology encompassing data collection from diverse Ainu text sources, meticulous preprocessing, and the deployment of neural MT models, culminating in the achievement of significant SacreBLEU scores that underscore the models' translation accuracy. The findings illustrate the potential of advanced MT technology to facilitate linguistic preservation and educational endeavors, advocating for integrating such technologies in safeguarding endangered languages. This research not only underscores the critical role of MT in bridging language divides but also sets a precedent for employing computational linguistics to preserve cultural and linguistic heritage.


Volume: NLP4DH
Section: Digital humanities in languages
Published on: April 29, 2024
Accepted on: April 9, 2024
Submitted on: February 29, 2024
Keywords: NLP,Machine Translation,Ainu,Digital Humanities,Japanese,Language Preservation,Language Revitalization,Linguistics,Low-Resource Language,Under-Resource Language

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