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Hybrid Machine Translation: Choosing the best translation with Support Vector Machines
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In the field of machine translation there are various systems available which have different strengths and weaknesses. This thesis investigates the combination of two systems, a rule based one and a statistical one, to see if such a hybrid system can provide higher quality translations. The classification approach was taken, where a support vector machine is used to choose which sentences from each of the two systems result in the best translation. To label the sentences from the collected data a new method of simulated annealing was applied and compared to previously tried heuristics. The results show that a hybrid system has an increased average BLEU score of 6.10% or 1.86 points over the single best system, and that using the labels created through simulated annealing, over heuristic rules, gives a significant improvement in classifier performance.

Place, publisher, year, edition, pages
2016. , 60 p.
IT, 16074
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-304257OAI: diva2:1014894
Educational program
Bachelor Programme in Computer Science
Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2016-10-03Bibliographically approved

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