Skip to main content
Date Topic Readings (starred=graduate level)
Jan 26 (snow day)
Jan 28 Introduction
Feb 02 Basics in Language and Probability
  • Koehn, Chapter 2.1 and 3
Feb 04 Language Models
  • Koehn, Chapter 7
Feb 09 IBM Model 1 and the EM Algorithm
Feb 11 Advanced Alignment Models
Feb 16 Phrase-Based Models
Feb 18 Decoding

(Demo)

Feb 23 Evaluation
Feb 25 Tuning
  • Koehn, Chapter 9.1-9.3
Mar 01 Sparse Features
Mar 03 Introduction to Moses
Mar 08 Corpus Acquisition from the Internet
Mar 10 (no class)
Mar 22 Reordering
Mar 24 Morphology
Mar 29 Syntax-Based Models
  • Koehn, Chapter 11.1-11.2
Mar 31 Syntax-Based Decoding I
  • Koehn, Chapter 11.3
Apr 05 Syntax-Based Decoding II
Apr 07 Semantics
Apr 12 Introduction to Neural Networks
Apr 14 Neural Language Models
Apr 19 Neural Translation Models
Apr 21 Training Neural Networks
Apr 26 Computer Aided Translation