Using linguistic knowledge in statistical machine translation
Name
710154183-MIT.pdf
Description
Full printable version
Size
10.49 MB
Format
Adobe PDF
Checksum (MD5)
c2b7e9df3f946b5756b30d89eecdf6e1
Author(s)
Zbib, Rabih M. (Rabih Mohamed), 1974-
Advisor(s)
James R. Glass and Steven R. Lerman.
Alternative Title
Using linguistic knowledge in SMT
Date Issued
2010
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, we present methods for using linguistically motivated information to enhance the performance of statistical machine translation (SMT). One of the advantages of the statistical approach to machine translation is that it is largely language-agnostic. Machine learning models are used to automatically learn translation patterns from data. SMT can, however, be improved by using linguistic knowledge to address specific areas of the translation process, where translations would be hard to learn fully automatically. We present methods that use linguistic knowledge at various levels to improve statistical machine translation, focusing on Arabic-English translation as a case study. In the first part, morphological information is used to preprocess the Arabic text for Arabic-to-English and English-to-Arabic translation, which reduces the gap in the complexity of the morphology between Arabic and English. The second method addresses the issue of long-distance reordering in translation to account for the difference in the syntax of the two languages. In the third part, we show how additional local context information on the source side is incorporated, which helps reduce lexical ambiguity. Two methods are proposed for using binary decision trees to control the amount of context information introduced. These methods are successfully applied to the use of diacritized Arabic source in Arabic-to-English translation. The final method combines the outputs of an SMT system and a Rule-based MT (RBMT) system, taking advantage of the flexibility of the statistical approach and the rich linguistic knowledge embedded in the rule-based MT system.
Description
Thesis (Ph. D. in Information Technology)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 153-162).
Subjects
Civil and Environmental Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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