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Machine Translation Optimization using Hybrid Architectures
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 10 - Number 9 |
| Year of Publication: 2016 |
| Authors: Neeha Ashraf, Manzoor Ahmad |
10.5120/ijais2016451548
|
Neeha Ashraf, Manzoor Ahmad . Machine Translation Optimization using Hybrid Architectures. International Journal of Applied Information Systems. 10, 9 ( May 2016), 19-25. DOI=10.5120/ijais2016451548
Abstract
Humans learn their culture through language. Its inquisitive nature of human and passion to travel across the world, warrants different cultures interact with each other, the means to achieve this is through human language, often interacting cultures communicate through different languages. To add to this, off-late, the increased use of social networking and the flood of information in foreign languages through web, the use of machine translation technology became inevitably significant. Researchers have predominantly employed two approaches in machine translations, based either on rule-based or statistical approaches. Both approaches have their strengths and weaknesses. Researchers working on both Rule-based and Statistical approaches have shown a keen interest in the area of hybrid machine translation. These systems try to profit from other respective approaches, combining data-driven and knowledge-driven elements. The interest is to investigate different hybrid arrangements required for optimizing the machine translations which will contribute to an overall increase in MT quality.
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