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Date Topic Readings (starred=graduate level)
Jan 27 Introduction
Jan 29 Basics in Language and Probability
  • Koehn, Chapter 2.1 and 3
Feb 03 Language Models
  • Koehn, Chapter 7
Feb 05 IBM Model 1 and the EM Algorithm
Feb 10 (no class)
Feb 12 Advanced Alignment Models
Feb 17 Phrase-Based Models
Feb 19 Decoding

(Demo)

Feb 24 Evaluation
Feb 26 Tuning
  • Koehn, Chapter 9.1-9.3
Mar 03 Sparse Features
Mar 05 (snow day)
Mar 10 Reordering
Mar 12 Introduction to Moses
Mar 24 Corpus Acquisition from the Internet
  • Guest lecture, Christian Buck (University of Edinburgh)
Mar 26 Morphology
Mar 31 Syntax-Based Models
  • Koehn, Chapter 11.1-11.2
Apr 02 Syntax-Based Decoding I
  • Koehn, Chapter 11.3
Apr 07 Syntax-Based Decoding II
Apr 09 Semantics
Apr 14 Introduction to Neural Networks
Apr 16 Neural Language Models
Apr 21 Neural Translation Models
Apr 23 Neural Components and Neural Translation
Apr 28 (school closed)
Apr 30 Computer Aided Translation