C S 479
Introduction to Machine Translation
Computer Science
College of Physical and Mathematical Sciences
Course Description
Evolution of machine translation technologies and algorithms, with a foundation in basic algorithms, human-machine interaction, automatic adaptation, statistical and neural models, multilingual models, multimodal models, quality evaluation and estimation, and speech-to-speech translation.
When Taught
Fall
Grade Rule
Grade Rule 8: A, B, C, D, E, I (Standard grade rule)
Fixed
3
Fixed
3
Title
MT Technology Evolution
Learning Outcome
Students will understand how the challenges of producing quality translations have been addressed by past and present technologies
Title
Building blocks of MT Technology
Learning Outcome
Students will be able to prepare data to be used to train MT systems, apply and interpret quality metrics to MT output, and implement basic MT-related algorithms
Title
MT Tools and Platforms
Learning Outcome
Students will be able to identify and use available platforms and tools to create MT systems for selected languages
Title
MT Applications
Learning Outcome
Students will be able to create and incorporate MT systems and/or MT-related functionality in a selected scenario or application
Title
Advanced MT algorithms
Learning Outcome
Students will become familiar with and be able to implement and use basic versions of certain more advanced MT algorithms used in current MT research
