Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2152
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dc.contributor.authorJoseph, JK-
dc.contributor.authorChathurika, W. M. T-
dc.contributor.authorNugaliyadde, A-
dc.contributor.authorMallawarachchi, Y-
dc.date.accessioned2022-05-03T03:34:08Z-
dc.date.available2022-05-03T03:34:08Z-
dc.date.issued2019-07-06-
dc.identifier.citationarXiv:1907.03202 [cs.CL] (or arXiv:1907.03202v1 [cs.CL]en_US
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/2152-
dc.description.abstractMachine Translation (MT) is an area in natural language processing, which focus on translating from one language to another. Many approaches ranging from statistical methods to deep learning approaches are used in order to achieve MT. However, these methods either require a large number of data or a clear understanding about the language. Sinhala language has less digital text which could be used to train a deep neural network. Furthermore, Sinhala has complex rules therefore, it is harder to create statistical rules in order to apply statistical methods in MT. This research focuses on Sinhala to English translation using an Evolutionary Algorithm (EA). EA is used to identifying the correct meaning of Sinhala text and to translate it to English. The Sinhala text is passed to identify the meaning in order to get the correct meaning of the sentence. With the use of the EA the translation is carried out. The translated text is passed on to grammatically correct the sentence. This has shown to achieve accurate resultsen_US
dc.language.isoenen_US
dc.publisherarXiv preprint arXiv:1907.03202en_US
dc.relation.ispartofseriesarXiv preprint arXiv:1907.03202;-
dc.subjectMachine Translationen_US
dc.subjectEvolutionary Algorithmen_US
dc.subjectNatural Language Processingen_US
dc.titleEvolutionary algorithm for sinhala to english translationen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.48550/arXiv.1907.03202en_US
Appears in Collections:Research Papers - Open Access Research
Research Papers - SLIIT Staff Publications
Research Publications -Dept of Information Technology

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