Courses

Machine Translation (11-731)

Units: 12
Semester: Spring (offered every other year)
Instructor(s): Alon Lavie, Stephan Vogel
Course home page: http://www-2.cs.cmu.edu/afs/cs/project/cmt-55/lti/Courses/731/www/
Prerequisites:

• 11-721 "Grammars and Lexicons" or equivalent background is recommended.
• 11-711 "Algorithms for NLP" or equivalent background is recommended.

Course Description:

Machine Translation is an introductory graduate-level course surveying history, techniques, and research topics in the field.

The main objectives of the course are:

• Obtain a basic understanding of MT systems and MT-related issues.
• Learn about theory and approaches in Machine Translation.
• Learn about basic techniques for MT development, in preparation for the MT Lab course and real-world MT system project development.
• Obtain in-depth knowledge of one current topic in MT, or
  Perform an analysis of a given MT problem, matching it with the most suitable techniques (includes research, report and presentation).

Course Topics:

1. Introduction to MT
2. History of MT
3. Modern Theory and Approaches for MT
   • Transfer Methods
   • Interlingua MT
   • Example-based MT
   • Statistical MT
   • Multi-Engine MT
4. MT System Development
   • Domain Analysis and System Spec.
   • Analyzer SW Development
   • Generator SW Development
   • Linguistic Knowledge Development
5. Issues and Other Topics in MT
   • Ambiguity & Ambiguity Resolution
   • Controlled Language Input/Output
   • Speech-to-Speech Translation
   • MT Workflow and Human Factors
   • MT Evaluation
   • What is a useful MT system?
   • Commercial MT Systems
   • Future of MT
6. Term Project Presentation & Discussion

Recommended Reading:

W. John Hutchins and Harold L. Somers, "An Introduction to Machine Translation",
Academic Press, San Diego, 1992.

Arturo Trujillo, "Translation Engines: Techniques for Machine Translation"
Springer-Verlag Series on Applied Computing, 1999.

Grading Criteria:

Students will be graded based on their performance on the following tasks:

• Homework: 2-3 assignments on lecture material
• Exams: In-class, close book
• Term Project: Class presentation, written paper
• Class Participation