Statistical Machine Translation

Date	Topic	Readings (starred=graduate level)
Sep 04	Introduction	Koehn (2010), Statistical Machine Translation, Chapter 1.2 Weaver (1949), Translation Kay, Translation
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	Koehn (2010), Statistical Machine Translation, Chapter 4.1-4.2 Knight (1997), Automating Knowledge Acquisition for Machine Translation
Sep 18	Phrase-Based Models	Koehn (2010), Statistical Machine Translation, Chapter 5 Durrani et al. (2013), Can Markov Models Over Minimal Translation Units Help Phrase-Based SMT?
Sep 20	Decoding (Demo)	Koehn (2010), Statistical Machine Translation, Chapter 6 Heafield et al. (2014), Faster Phrase-Based Decoding by Refining Feature State
Sep 25	Evaluation	Koehn (2010), Statistical Machine Translation, Chapter 8 Graham et al. (2014), Is Machine Translation Getting Better over Time?
Sep 27	Introduction to Neural Networks	Koehn (2019), Neural Machine Translation, Chapter 4 Makin (2006), Backpropagation LeCun et al. (1998), Efficient BackProp Srivastava et al. (2014), Dropout - A Simple Way to Prevent Neural Networks from Overfitting
Oct 02	Computation Graphs	Koehn (2019), Neural Machine Translation, Chapter 5 Tensorflow, Neural network online demo
Oct 04	Neural Language Models	Koehn (2019), Neural Machine Translation, Chapter 6 Bengio et al. (2002), A Neural Probabilistic Language Model Tomas Mikolov (2012), Statistical Language Models Based on Neural Networks, Chapter 3 Karpathy et al. (2015), Visualizing and Understanding Recurrent Networks
Oct 09	Neural Translation Models	Koehn (2019), Neural Machine Translation, Chapter 7 Bahdanau et al. (2015), Neural Machine Translation by Jointly Learning to Align and Translate
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	Koehn (2019), Neural Machine Translation, Chapter 10 Vaswani et al. (2017), Attention Is All You Need Gehring et al. (2017), Convolutional Sequence to Sequence Learning
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	Koehn (2019), Neural Machine Translation, Chapter 14 Cook et al. (2011), The World Color Survey Database - History and Use Banarescu et al. (2013), Abstract Meaning Representation for Sembanking
Oct 30	Adaptation	Koehn (2019), Neural Machine Translation, Chapter 12 Axelrod et al. (2011), Domain adaptation via pseudo in-domain data selection
Nov 01	Beyond Parallel Data	Koehn (2019), Neural Machine Translation, Chapter 13
Nov 06	Current Challenges	Koehn (2019), Neural Machine Translation, Chapter 15 Koehn and Knowles (2017), Six Challenges for Neural Machine Translation Khayrallah and Koehn (2018), On the Impact of Various Types of Noise on Neural Machine Translation
Nov 08	Analysis and Visualization	Koehn (2019), Neural Machine Translation, Chapter 16
Nov 13	Corpus Acquisition from the Internet	Uszkoreit et al. (2010), Large Scale Parallel Document Mining for Machine Translation Resnik and Smith (2003), The Web as a Parallel Corpus Buck and Heafield (2014), N-gram Counts and Language Models from the Common Crawl Koehn et al. (2018), Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering
Nov 15	Computer Aided Translation	Barrachina et al. (2009), Statistical Approaches to Computer-Assisted Translation Knowles and Koehn (2017), Neural Interactive Machine Translation
Nov 27	Project Presentations
Nov 29	Project Presentations
Dec 04	Project Presentations
Dec 06	Project Presentations