Conference paper
Evolutionary algorithm for Sinhala to English translation
2019 National Information Technology Conference (NITC)
National Information Technology Conference (NITC) 2019 (Colombo, Sri Lanka, 08/10/2019–10/10/2019)
2019
Abstract
Machine Translation (MT) is an area in natural language processing, which focuses 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, and 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 into 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 results.
Details
- Title
- Evolutionary algorithm for Sinhala to English translation
- Authors/Creators
- A. Nugaliyadde (Author/Creator) - Murdoch UniversityJ.K. Joseph (Author/Creator) - Sri Lanka Institute of Information TechnologyW.M.T. Chathurika (Author/Creator) - Sri Lanka Institute of Information TechnologyY. Mallawarachchi (Author/Creator) - Sri Lanka Institute of Information Technology
- Publication Details
- 2019 National Information Technology Conference (NITC)
- Conference
- National Information Technology Conference (NITC) 2019 (Colombo, Sri Lanka, 08/10/2019–10/10/2019)
- Identifiers
- 991005542565307891
- Murdoch Affiliation
- College of Science, Health, Engineering and Education
- Language
- English
- Resource Type
- Conference paper
Metrics
119 Record Views