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Date Topic Readings (starred=graduate level)
Sep 04 Introduction
Sep 06 Basics in Language and Probability
  • Koehn (2010), Statistical Machine Translation, Chapter 2.1 and 3
Sep 11 Language Models
  • Koehn (2010), Statistical Machine Translation, Chapter 7
Sep 13 IBM Model 1 and the EM Algorithm
Sep 18 Phrase-Based Models
Sep 20 Decoding

(Demo)

Sep 25 Evaluation
Sep 27 Introduction to Neural Networks
Oct 02 Computation Graphs
Oct 04 Neural Language Models
Oct 09 Neural Translation Models
Oct 11 Decoding in Neural Translation Models
  • Koehn (2019), Neural Machine Translation, Chapter 8
Oct 16 Machine Learning Tricks
  • Koehn (2019), Neural Machine Translation, Chapter 9
Oct 18 Alternative Architectures
Oct 23 Words and Morphology
  • Koehn (2010), Statistical Machine Translation, Chapter 10.2
  • Koehn (2019), Neural Machine Translation, Chapter 11
Oct 25 Syntax and Semantics
Oct 30 Adaptation
Nov 01 Beyond Parallel Data
  • Koehn (2019), Neural Machine Translation, Chapter 13
Nov 06 Current Challenges
Nov 08 Analysis and Visualization
  • Koehn (2019), Neural Machine Translation, Chapter 16
Nov 13 Corpus Acquisition from the Internet
Nov 15 Computer Aided Translation
Nov 27 Project Presentations
Nov 29 Project Presentations
Dec 04 Project Presentations
Dec 06 Project Presentations