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Relevant bibliographies by topics / English-Wolaytta Machine Translation

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Academic literature on the topic 'English-Wolaytta Machine Translation'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "English-Wolaytta Machine Translation"

1

Tonja, Atnafu Lambebo, Olga Kolesnikova, Alexander Gelbukh, and Grigori Sidorov. "Low-Resource Neural Machine Translation Improvement Using Source-Side Monolingual Data." Applied Sciences 13, no. 2 (2023): 1201. http://dx.doi.org/10.3390/app13021201.

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Despite the many proposals to solve the neural machine translation (NMT) problem of low-resource languages, it continues to be difficult. The issue becomes even more complicated when few resources cover only a single domain. In this paper, we discuss the applicability of a source-side monolingual dataset of low-resource languages to improve the NMT system for such languages. In our experiments, we used Wolaytta–English translation as a low-resource language. We discuss the use of self-learning and fine-tuning approaches to improve the NMT system for Wolaytta–English translation using both auth

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2

Elisaye, Bekele Milke. "Bidirectional English to Wolaytta Machine Translation Using a Hybrid Approach." International Journal of Soft Computing and Engineering (IJSCE) 15, no. 2 (2025): 1–10. https://doi.org/10.35940/ijsce.B1028.15020525/.

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<strong>Abstract: </strong>As a part of natural language processing (NLP), machine translation focuses on automated techniques to produce target language text from the source language text. In this study, we combined two approaches: the rule-based MT approach and the statistical MT approach. Sentence reordering, Language model, Translation models, and decoding comprise the system. POS tagging was used to reorder the sentence more comparably, the IRSTLM tool was used to create language models for English, and the Wolaytta, Giza++ tool was used for translation. To ensure mutual translation, two

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3

Elisaye, Bekele Milke. "Bidirectional English to Wolaytta Machine Translation Using Hybrid Approach." International Journal of Soft Computing and Engineering (IJSCE) 15, no. 2 (2025): 1–10. https://doi.org/10.35940/ijsce.B1028.15020525.

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<strong>Abstract:</strong> As a part of natural language processing (NLP), machine translation focuses on automated techniques to produce target language text from the source language text. In this study, we combined two approaches: the rule-based MT approach and the statistical MT approach. Sentence reordering, Language model, Translation models, and decoding comprise the system. POS tagging was used to reorder the sentence more comparably, the IRSTLM tool was used to create language models for English, and the Wolaytta, Giza++ tool was used for translation. To ensure mutual translation, two

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4

Milke, Elisaye Bekele, Tibebe Beshah Tesema, and Mesfin Leranso Betalo. "Bidirectional English to Wolaytta Machine Translation Using a Hybrid Approach." International Journal of Soft Computing and Engineering 15, no. 2 (2025): 1–10. https://doi.org/10.35940/ijsce.b1028.15020525.

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Abstract:

As a part of natural language processing (NLP), machine translation focuses on automated techniques to produce target language text from the source language text. In this study, we combined two approaches: the rule-based MT approach and the statistical MT approach. Sentence reordering, Language model, Translation models, and decoding comprise the system. POS tagging was used to reorder the sentence more comparably, the IRSTLM tool was used to create language models for English, and the Wolaytta, Giza++ tool was used for translation. To ensure mutual translation, two language models have been d

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Conference papers on the topic "English-Wolaytta Machine Translation"

1

Bedecho, Aklilu Thomas, and Michael Melese Woldeyohannis. "Wolaytta-English Cross-lingual Information Retrieval using Neural Machine Translation." In 2022 International Conference on Information and Communication Technology for Development for Africa (ICT4DA). IEEE, 2022. http://dx.doi.org/10.1109/ict4da56482.2022.9971267.

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