Exploring the evolution and future prospects of Amharic to English machine translation: a systematic review
IntroductionIn the last couple of decades, Amharic-English translation has greatly improved from a rule-based approach to contemporary systems that apply neural networks. Even after these advancements, problems remain because of the Amharic language’s resource-scarce nature, such as inadequate datas...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1456245/full |
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| author | Muluken Hussen Asebel Shimelis Getu Assefa Mesfin Abebe Haile |
| author_facet | Muluken Hussen Asebel Shimelis Getu Assefa Mesfin Abebe Haile |
| author_sort | Muluken Hussen Asebel |
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| description | IntroductionIn the last couple of decades, Amharic-English translation has greatly improved from a rule-based approach to contemporary systems that apply neural networks. Even after these advancements, problems remain because of the Amharic language’s resource-scarce nature, such as inadequate datasets, tools for working with the language, and the intricate semantics and grammar of Amharic as compared to English. This systematic review seeks to analyze the evolution of the Amharic-English machine translation, the prominent ongoing difficulties, the noteworthy research undertakings, and the prospects of the research focus.MethodsThis review uses a systematic approach to study the literature on Amharic-English machine translation. Important documents were retrieved from academic websites, and those with relevance to the methodologies of machine translation, language resources development, and evaluation practices were chosen. Primarily, the focus was on both statistical and neural machine translation models, especially those with transformer structures.ResultsThe initial attempts to translate English to Amharic and vice-versa relied on statistic machine translation (SMT), which set the stage for the evolution to neural machine translation (NMT). The use of transformer models has impacted the accuracy and fluidity of translations tremendously. Still, there is a lack of sufficient parallel corpora, effective methods for tokenization of Amharic, and other resources. Recently, the focus has been on creating new datasets, improving token-level engineering, and modifying NMT models for Amharic’s complex morphological structure.DiscussionThe complete solutions for enhancing Amharic-English translation remain elusive and include the lack of sufficient data, semantic correspondence, and grammatical consistency within and across translations. Pursuable avenues include augmentation of data, tokenization on the language level, and incorporation of linguistic elements into the parallel corpora. In addition, creating effective evaluation frameworks along with comprehensive linguistic data is important for assessing and improving translation tools. With these changes, cross-cultural interaction and increasing accessibility to modern technologies will be achieved. |
| format | Article |
| id | doaj-art-87f426134cf24cb1bc71ee6c52c8b015 |
| institution | DOAJ |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-87f426134cf24cb1bc71ee6c52c8b0152025-08-20T03:13:29ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-05-01810.3389/frai.2025.14562451456245Exploring the evolution and future prospects of Amharic to English machine translation: a systematic reviewMuluken Hussen Asebel0Shimelis Getu Assefa1Mesfin Abebe Haile2Department of Computer Science and Engineering, School of Electrical Engineering and Computing, Adama Science and Technology University, Adama, EthiopiaDepartment of Research Methods and Information Science, Morgridge College of Education, University of Denver, Denver, CO, United StatesDepartment of Computer Science and Engineering, School of Electrical Engineering and Computing, Adama Science and Technology University, Adama, EthiopiaIntroductionIn the last couple of decades, Amharic-English translation has greatly improved from a rule-based approach to contemporary systems that apply neural networks. Even after these advancements, problems remain because of the Amharic language’s resource-scarce nature, such as inadequate datasets, tools for working with the language, and the intricate semantics and grammar of Amharic as compared to English. This systematic review seeks to analyze the evolution of the Amharic-English machine translation, the prominent ongoing difficulties, the noteworthy research undertakings, and the prospects of the research focus.MethodsThis review uses a systematic approach to study the literature on Amharic-English machine translation. Important documents were retrieved from academic websites, and those with relevance to the methodologies of machine translation, language resources development, and evaluation practices were chosen. Primarily, the focus was on both statistical and neural machine translation models, especially those with transformer structures.ResultsThe initial attempts to translate English to Amharic and vice-versa relied on statistic machine translation (SMT), which set the stage for the evolution to neural machine translation (NMT). The use of transformer models has impacted the accuracy and fluidity of translations tremendously. Still, there is a lack of sufficient parallel corpora, effective methods for tokenization of Amharic, and other resources. Recently, the focus has been on creating new datasets, improving token-level engineering, and modifying NMT models for Amharic’s complex morphological structure.DiscussionThe complete solutions for enhancing Amharic-English translation remain elusive and include the lack of sufficient data, semantic correspondence, and grammatical consistency within and across translations. Pursuable avenues include augmentation of data, tokenization on the language level, and incorporation of linguistic elements into the parallel corpora. In addition, creating effective evaluation frameworks along with comprehensive linguistic data is important for assessing and improving translation tools. With these changes, cross-cultural interaction and increasing accessibility to modern technologies will be achieved.https://www.frontiersin.org/articles/10.3389/frai.2025.1456245/fullmachine translationAmharicEnglishsystematic reviewlow-resource languages |
| spellingShingle | Muluken Hussen Asebel Shimelis Getu Assefa Mesfin Abebe Haile Exploring the evolution and future prospects of Amharic to English machine translation: a systematic review Frontiers in Artificial Intelligence machine translation Amharic English systematic review low-resource languages |
| title | Exploring the evolution and future prospects of Amharic to English machine translation: a systematic review |
| title_full | Exploring the evolution and future prospects of Amharic to English machine translation: a systematic review |
| title_fullStr | Exploring the evolution and future prospects of Amharic to English machine translation: a systematic review |
| title_full_unstemmed | Exploring the evolution and future prospects of Amharic to English machine translation: a systematic review |
| title_short | Exploring the evolution and future prospects of Amharic to English machine translation: a systematic review |
| title_sort | exploring the evolution and future prospects of amharic to english machine translation a systematic review |
| topic | machine translation Amharic English systematic review low-resource languages |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1456245/full |
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