Quality Assessment of Translators Using Deep Neural Networks for Polish-English and English-Polish Translation

Pilch, Agnieszka; Zygała, Ryszard; Gryncewicz, Wiesława

doi:10.1109/acit54803.2022.9913189

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…The study highlights the imperative for ongoing advancement in MT approaches for languages with minimal resources, especially when conventional linguistic resources are sparse or absent. By merging insights from Natural Language Processing (NLP), MT, and language conservation efforts, our research provides a model for future studies and underscores the critical need for innovation in these domains Pilch et al [2022].…”

Section: Discussionmentioning

confidence: 97%

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

Miyagawa

2024

Journal of Data Mining &Amp; Digital Humanities

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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.

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Section: Discussionmentioning

confidence: 97%

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

Miyagawa

2024

Journal of Data Mining &Amp; Digital Humanities

View full text Add to dashboard Cite

show abstract

Quality Assessment of Translators Using Deep Neural Networks for Polish-English and English-Polish Translation

Cited by 1 publication

References 6 publications

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

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

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