NLP267: Machine Translation

Machine Translation systems can instantly translate between any pair of over eighty human languages such as German to English or French to Russian. Modern translation systems learn to translate by reading millions of words of already translated text. This course covers the models and algorithms used by such systems and explains how they are able to automatically translate one human language to another. The course covers fundamental building blocks using concepts from linguistics, statistical and deep machine learning, algorithms, and data structures. It provides insight into the challenges associated with machine translation and introduces novel approaches that might lead to better machine translation systems. Prerequisite(s): NLP 201 and NLP 243 or CSE 244B. Concurrent or previous enrollment in NLP 202. Enrollment is restricted to natural language processing graduate students and computer science and engineering Ph.D. students, or by permission of the instructor.

5 credits

Year	Fall	Winter	Spring	Summer
2022-23		Section 50 Ian Lane (ialane)

While the information on this web site is usually the most up to date, in the event of a discrepancy please contact your adviser to confirm which information is correct.