Hybrid Machine Translation: Choosing the best translation with Support Vector Machines
Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
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.
Engineering and Technology
IdentifiersURN: urn:nbn:se:uu:diva-304257OAI: oai:DiVA.org:uu-304257DiVA: diva2:1014894
Bachelor Programme in Computer Science
Gällmo, OllePearson, Justin