Machine Translation and Sequence-to-Sequence Models

Catalog description

Instructors: Graham Neubig. Prerequisites: This course has no official pre-requisites, although 11-711 "Algorithms for NLP" or 10-701 "Machine Learning" would be helpful. Course Description: Machine Translation and Sequence-to-Sequence Models is an introductory graduate-level course surveying the primary approaches and methods for developing systems to translate between human languages, or other sequential data. The main objective of the course is to obtain basic understanding and implementation skills for modern methods for MT and sequence transduction, including how to design models, how to learn the model parameters, how to search for the best output, and how to create training data. The course will focus on machine translation, but also briefly cover tasks such as dialog response generation, image caption generation, and others.