Kernel-Based Machine Translation
Kernel-Based Machine Translation
This chapter presents a novel framework for machine translation based on kernel ridge regression. As a kernel method, the framework has the advantage of capturing the correspondences among the features of inputs and outputs in a very high-dimensional space. But the drawback is that its computational complexities are much higher than probabilistic models. A solution is sparse approximation, which poses the problem of extracting a sufficient amount of relevant bilingual training samples for a given input. Other essential improvements to this model could be the integration of additional language models and the utilization of linguistic knowledge.
Keywords: machine translation, kernel ridge regression, sparse approximation, decoding, language processing
MIT Press Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.
Please, subscribe or login to access full text content.
If you think you should have access to this title, please contact your librarian.
To troubleshoot, please check our FAQs, and if you can't find the answer there, please contact us.
