Statistical Machine Translation

Date	Topic	Readings (starred=graduate level)
Jan 27	Introduction	Koehn, Chapter 1.2 Weaver (1949), Translation Kay, Translation
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	Koehn, Chapter 4.1-4.2 Knight (1997), Automating Knowledge Acquisition for Machine Translation
Feb 10	(no class)
Feb 12	Advanced Alignment Models	Koehn, Chapter 4.4 Dyer et al. (2013), A Simple, Fast, and Effective Reparameterization of IBM Model 2 Vogel et al. (1996), HMM-Based Word Alignment in Statistical Translation
Feb 17	Phrase-Based Models	Koehn, Chapter 5 Durrani et al. (2013), Can Markov Models Over Minimal Translation Units Help Phrase-Based SMT?
Feb 19	Decoding (Demo)	Koehn, Chapter 6 Heafield et al. (2014), Faster Phrase-Based Decoding by Refining Feature State
Feb 24	Evaluation	Koehn, Chapter 8 Graham et al. (2014), Is Machine Translation Getting Better over Time?
Feb 26	Tuning	Koehn, Chapter 9.1-9.3
Mar 03	Sparse Features	Koehn, Chapter 9.4 Chiang (2012), Hope and Fear for Discriminative Training of Statistical Translation Models Cherry and Foster (2012), Batch Tuning Strategies for Statistical Machine Translation Hopkins and May (2011), Tuning as Ranking Wuebker et al. (2012), Leave-One-Out Phrase Model Training for Large-Scale Deployment Yu et al. (2013), Max-Violation Perceptron and Forced Decoding for Scalable MT Training
Mar 05	(snow day)
Mar 10	Reordering	Koehn, Chapter 10.3 Genzel (2010), Automatically Learning Source-side Reordering Rules for Large Scale Machine Translation Bisazza and Federico (2010), Chunk-based Verb Reordering in VSO Sentences for Arabic-English Statistical Machine Translation
Mar 12	Introduction to Moses
Mar 24	Corpus Acquisition from the Internet	Guest lecture, Christian Buck (University of Edinburgh)
Mar 26	Morphology	Koehn, Chapter 10.2 Stymne et al. (2013), Generation of Compound Words in Statistical Machine Translation into Compounding Languages Fraser et al. (2012), Modeling Inflection and Word-Formation in SMT Chahuneau et al. (2013), Translating into Morphologically Rich Languages with Synthetic Phrases
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	Koehn, Chapter 11.3 Hopkins and Langmead (2010), SCFG Decoding Without Binarization Sennrich (2014), A CYK+ Variant for SCFG Decoding Without a Dot Chart
Apr 09	Semantics	Cook et al. (2011), The World Color Survey Database - History and Use Banarescu et al. (2013), Abstract Meaning Representation for Sembanking
Apr 14	Introduction to Neural Networks	Koehn (2014), Neural Network Handout Makin (2006), Backpropagation
Apr 16	Neural Language Models	Bengio et al. (2002), A Neural Probabilistic Language Model Tomas Mikolov, Statistical Language Models Based on Neural Networks, Chapter 3
Apr 21	Neural Translation Models	Guest lecture, Gaurav Kumar (Johns Hopkins University) Zhang et al. (2014), Bilingually-constrained Phrase Embeddings for Machine Translation Devlin et al. (2014), Fast and Robust Neural Network Joint Models for Statistical Machine Translation Bahdanau et al. (2015), Neural Machine Translation by Jointly Learning to Align and Translate
Apr 23	Neural Components and Neural Translation	Sennrich (2015), Modelling and Optimizing on Syntactic N-Grams for Statistical Machine Translation Kalchbrenner and Blunsom (2013), Recurrent Continuous Translation Models
Apr 28	(school closed)
Apr 30	Computer Aided Translation	Barrachina et al. (2009), Statistical Approaches to Computer-Assisted Translation Germann (2014), Dynamic Phrase Tables for Machine Translation in an Interactive Post-editing Scenario