Bidirectional English to Wolaytta Machine Translation Using Hybrid Approach

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Elisaye Bekele Milke
Tibebe Beshah Tesema
Mesfin Leranso Betalo

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 developed. Four phases of experiments are carried out on the collected data set. Phases of experimentation include preprocessing on the parallel corpus, language modeling, training the translation model, and tune-up the translation system. For both side translations, the BLEU score assessed the accuracy of the translation from Wolaytta to English was 46.31 % and from English to Wolaytta was 56.56%.

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[1]
Elisaye Bekele Milke, Tibebe Beshah Tesema, and Mesfin Leranso Betalo, “Bidirectional English to Wolaytta Machine Translation Using Hybrid Approach”, IJSCE, vol. 15, no. 2, pp. 1–10, May 2025, doi: 10.35940/ijsce.B1028.15020525.

References

Kim, M. K., Takero, H., & Fedovik, S. (2023). Universal Syntactic Structures: Modeling Syntax for Various Natural Languages. arXiv preprint arXiv:2402.01641. DOI: https://doi.org/10.48550/arXiv.2402.01641

Ashkanasy, N. M., Trevor-Roberts, E., & Earnshaw, L. (2002). The Anglo cluster: Legacy of the British empire. Journal of World Business, 37(1), 28-39. DOI: https://doi.org/10.1016/S1090-9516(01)00072-4

Bade, G. Y., & Seid, H. (2018). Development of Longest-Match Based Stemmer for Texts of Wolaita Language. Vol, 4, 79-83. DOI: https://doi.org/10.11648/j.ijdst.20180403.11

Bedecho, A. T., & Bokka, R. K. (2024). Development of Sentiment Analysis for the Wolaita Language using Machine Learning Approaches. In 2024 International Conference on Information and Communication Technology for Development for Africa (ICT4DA) (pp. 178-182). IEEE. DOI: https://doi.org/10.1109/ICT4DA62874.2024.10777118

Ambushe, S. A., Awoke, N., Demissie, B. W., & Tekalign, T. (2023). Holistic nursing care practice and associated factors among nurses in public hospitals of Wolaita zone, South Ethiopia. BMC nursing, 22(1), 390. DOI: https://doi.org/10.1186/s12912-023-01517-0

Rossi, C. (2017). Introducing statistical machine translation in translator training: from uses and perceptions to course design, and back again. Revista Tradumàtica: tecnologies de la traducció, (15), 48. Doi : https://doi.org/10.5565/rev/tradumatica.195

Azath, M., & Kiros, T. (2020). Statistical machine translator for English to Tigrigna translation. International Journal of Scientific and Technology Research, 9(1), 2095-2099. DOI: https://www.readkong.com/page/statistical-machine-translator-for-english-to-tigrigna-1868057

Teshome, E. (2013). Bidirectional English-Amharic machine translation: an experiment using constrained corpus (Doctoral dissertation, Addis Ababa University). DOI: http://thesisbank.jhia.ac.ke/id/eprint/6064

Mara, M. (2018). English-Wolaytta Machine Translation using Statistical Approach (Doctoral dissertation, St. Mary's University). http://www.repository.smuc.edu.et/handle/123456789/4462

Tulu, G. (2022). Bidirectional AmharicAfaan Oromo Machine Translation Using Hybrid Approach. DOI: https://projectng.com/topic/co22921/bidirectional-amharic-afaan-oromo-machine#google_vignette

Shirko, B. F. (2020). Part of speech tagging for wolaita language using transformation-based learning (tbl) approach. DOI: https://www.researchgate.net/publication/345243262_Part_of_Speech_Tagging_for_Wolaita_Language_using_Transformation_based_Learning_TBL_Approach

Sinhal, R. A., & Gupta, K. O. (2014). Machine translation approaches and design aspects. IOSR Journal of Computer Engineering, 16(1), 22-25. DOI: https://doi.org/10.9790/0661-16122225

Koehn, P. (2009). Statistical machine translation. Cambridge University Press. Doi: https://doi.org/10.1017/CBO9780511815829

Chéragui, M. A. (2012). Theoretical Overview of Machine Translation. ICWIT, 160-169. DOI: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=aad01b2a642711ef0b4d7d89d8d50fc268a222ce

Phan, H., & Jannesari, A. (2020). Statistical machine translation outperforms neural machine translation in software engineering: why and how Proceedings of the 1st ACM SIGSOFT International Workshop on Representation Learning for Software Engineering and Program Languages, Virtual, USA. DOI: https://doi.org/10.1145/3416506.3423576

Thendral, R., & Sigappi, AN. (2020). Stacked Bidirectional Long Short Term Memory Models To Predict Protein Secondary Structure. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 3, pp. 1605–1608). DOI: https://doi.org/10.35940/ijitee.c8368.019320

Vidya, K., Annapoorani, P., Akila, S., & Vijayalakshmi, M. (2019). Microcontroller Based Bi-Directional DC-DC Converter for Automobile Application. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 2, pp. 2776–2778). DOI: https://doi.org/10.35940/ijeat.b2280.129219

S. T. Shenbagavalli, D. Shanthi, S. Naganandhini, R. Karthikeyan, Role of Deep Recurrent Neural Networks in Natural Language Processing. (2019). In International Journal of Recent Technology and Engineering (Vol. 8, Issue 2S11, pp. 4082–4084). DOI: https://doi.org/10.35940/ijrte.b1597.0982s1119

Krishna, G. G. (2023). Multilingual NLP. In International Journal of Advanced Engineering and Nano Technology (Vol. 10, Issue 6, pp. 9–12). DOI: https://doi.org/10.35940/ijaent.e4119.0610623

Patidar, C. P., Katara, Y., & Sharma, Dr. M. (2020). Hybrid News Recommendation System using TF-IDF and Similarity Weight Index. In International Journal of Soft Computing and Engineering (Vol. 10, Issue 3, pp. 5–9). DOI: https://doi.org/10.35940/ijsce.c3471.1110320

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