Fine-Tuning the mT5 Model on Bidirectional Myanmar and Tedim Chin Machine Translation System.
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| Title: | Fine-Tuning the mT5 Model on Bidirectional Myanmar and Tedim Chin Machine Translation System. |
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| Authors: | Man, Ciin Zam1 ciinzamman@ucsy.edu.mm, Win, Si Si Mar2 sisimarwin@ucsy.edu.mm, Khine, Kyi Lai Lai3 kyilailai67@gmail.com |
| Source: | IAENG International Journal of Computer Science. Dec2025, Vol. 52 Issue 12, p4589-4599. 11p. |
| Subjects: | Low-resource languages, Text processing (Computer science), Machine translating, Language & languages |
| Geographic Terms: | Myanmar |
| Abstract: | Nowadays, machine translation (MT) is a vital tool for overcoming language barriers, especially for underrepresented and low-resource languages. This study explores the effectiveness of the mT5 neural machine translation model in facilitating translation between Myanmar and Tedim Chin, two languages are limited digital resources. To conduct this research, we built a parallel corpus of 26,404 Myanmar-Tedim Chin sentence pairs of the general domain that are written in the Myanmar language. The data were collected from diverse domains and manually translated them into Tedim Chin, resulting in a custom Myanmar-Tedim Chin corpus. A significant challenge in processing Myanmar text is its lack of explicit word boundaries, which necessitates robust segmentation techniques. To address this, we implemented the syllable-level and word-level segmentation methods as part of the preprocessing step. The segmented data were then used to fine-tune the model, and the model's performance was evaluated using BLEU scores and accuracy metrics. Despite Tedim Chin being a low-resource language, the mT5 model achieved promising results by indicating its suitability for translation tasks involving both Myanmar and Tedim Chin. This study highlights the effectiveness of the mT5 model compared with the Transformer model, Helsinki-NLP model, and NLLB-200 model in advancing machine translation for underrepresented languages and provides a foundation for future research in this area. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 189696813 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Fine-Tuning the mT5 Model on Bidirectional Myanmar and Tedim Chin Machine Translation System. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Man%2C+Ciin+Zam%22">Man, Ciin Zam</searchLink><relatesTo>1</relatesTo><i> ciinzamman@ucsy.edu.mm</i><br /><searchLink fieldCode="AR" term="%22Win%2C+Si+Si+Mar%22">Win, Si Si Mar</searchLink><relatesTo>2</relatesTo><i> sisimarwin@ucsy.edu.mm</i><br /><searchLink fieldCode="AR" term="%22Khine%2C+Kyi+Lai+Lai%22">Khine, Kyi Lai Lai</searchLink><relatesTo>3</relatesTo><i> kyilailai67@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Dec2025, Vol. 52 Issue 12, p4589-4599. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Low-resource+languages%22">Low-resource languages</searchLink><br /><searchLink fieldCode="DE" term="%22Text+processing+%28Computer+science%29%22">Text processing (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+translating%22">Machine translating</searchLink><br /><searchLink fieldCode="DE" term="%22Language+%26+languages%22">Language & languages</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Myanmar%22">Myanmar</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Nowadays, machine translation (MT) is a vital tool for overcoming language barriers, especially for underrepresented and low-resource languages. This study explores the effectiveness of the mT5 neural machine translation model in facilitating translation between Myanmar and Tedim Chin, two languages are limited digital resources. To conduct this research, we built a parallel corpus of 26,404 Myanmar-Tedim Chin sentence pairs of the general domain that are written in the Myanmar language. The data were collected from diverse domains and manually translated them into Tedim Chin, resulting in a custom Myanmar-Tedim Chin corpus. A significant challenge in processing Myanmar text is its lack of explicit word boundaries, which necessitates robust segmentation techniques. To address this, we implemented the syllable-level and word-level segmentation methods as part of the preprocessing step. The segmented data were then used to fine-tune the model, and the model's performance was evaluated using BLEU scores and accuracy metrics. Despite Tedim Chin being a low-resource language, the mT5 model achieved promising results by indicating its suitability for translation tasks involving both Myanmar and Tedim Chin. This study highlights the effectiveness of the mT5 model compared with the Transformer model, Helsinki-NLP model, and NLLB-200 model in advancing machine translation for underrepresented languages and provides a foundation for future research in this area. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 4589 Subjects: – SubjectFull: Low-resource languages Type: general – SubjectFull: Text processing (Computer science) Type: general – SubjectFull: Machine translating Type: general – SubjectFull: Language & languages Type: general – SubjectFull: Myanmar Type: general Titles: – TitleFull: Fine-Tuning the mT5 Model on Bidirectional Myanmar and Tedim Chin Machine Translation System. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Man, Ciin Zam – PersonEntity: Name: NameFull: Win, Si Si Mar – PersonEntity: Name: NameFull: Khine, Kyi Lai Lai IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 1819656X Numbering: – Type: volume Value: 52 – Type: issue Value: 12 Titles: – TitleFull: IAENG International Journal of Computer Science Type: main |
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