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 
PhraseBased 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

