Statistical Machine Translation through Global Lexical Selection and Sentence Reconstruction
Statistical Machine Translation through Global Lexical Selection and Sentence Reconstruction
This chapter presents the details of training a global lexical selection model using classification techniques and sentence reconstruction models using permutation automata. It also provides a stochastic finite-state transducer (SFST) as an example of an approach that relies on local associations, and uses it to compare and contrast the approach. The chapter is organized as follows. Section 10.2 describes in detail the different stages used to train an SFST translation model, and discusses the steps in decoding a source input using the trained SFST model. Section 10.3 presents the global lexical selection and the sentence reconstruction models. Section 10.4 discusses the rationale for choosing the classifier to train the global lexical selection model. Section 10.5 reports the results of the two translation models on a few data sets and contrasts the strengths and limitations of the two approaches.
Keywords: training, decoding, lexical selection model, stochastic finite-state transducer, machine learning
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.
