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Myaamia Translator: Using Neural Machine Translation With Attention to Translate a Low-resource Language

Baaniya, Bishal
http://orcid.org/0009-0007-4093-0076
http://rave.ohiolink.edu/etdc/view?acc_num=miami1680776041086014

Abstract Details

2023, Master of Science, Miami University, Computer Science and Software Engineering.
It is a well-established fact that the performance of Machine Translation (MT) techniques largely depends on the quantity and quality of data available. The lack of a large well-curated dataset is especially a challenge for low-resource languages. The Myaamia language, also known as the Miami-Illinois language, is an endangered Native American language, and there are active efforts being made toward its revitalization. As a part of the revitalization process, the recorded texts are currently being manually translated, which might take up to a decade to translate at the current rate, according to some expert assessments. To speed up the translation process, we developed Myaamia Translator, a Neural Machine Translation (NMT) based machine translation approach, which leverages the state-of-the-art transformer architecture to translate text from Myaamia to English. The contributions of this work are two-fold: first, we use a combination of rule-based augmentation and back-translation augmentation to address the data limitation; and second, we train the model using the large dataset to test its effectiveness in translating a religious Myaamia textbook to English.
Christopher Vendome (Advisor)
David Costa (Committee Member)
Hakam Alomari (Committee Member)
Douglas Troy (Committee Member)
75 p.
Artificial Intelligence; Computer Engineering; Computer Science; Language; Linguistics; Native American Studies
Neural machine translation, Myaamia, Native American Language, Machine Learning, Data Augmentation, Rule-based Augmentation, Back-translation Augmentation, Transformer, BLEU, METEOR, Amazon Sagemaker, Low-resource machine translation

Recommended Citations

Citations

  • Baaniya, B. (2023). Myaamia Translator: Using Neural Machine Translation With Attention to Translate a Low-resource Language [Master's thesis, Miami University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=miami1680776041086014

    APA Style (7th edition)

  • Baaniya, Bishal. Myaamia Translator: Using Neural Machine Translation With Attention to Translate a Low-resource Language. 2023. Miami University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=miami1680776041086014.

    MLA Style (8th edition)

  • Baaniya, Bishal. "Myaamia Translator: Using Neural Machine Translation With Attention to Translate a Low-resource Language." Master's thesis, Miami University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=miami1680776041086014

    Chicago Manual of Style (17th edition)

miami1680776041086014
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© 2023, all rights reserved.
This open access ETD is published by Miami University and OhioLINK.