The success of statistical machine translation systems such as Moses, Language Weaver and Google Translate has shown that it is possible to build high performance machine translation systems with a small amount of effort using statistical learning techniques.
This course will present the basic modeling behind statistical machine translation in a concise way.
Email Address: SubstituteMyLastName@ims.uni-stuttgart.de
University of Stuttgart
DFG Project: Models of Morphosyntax for Statistical Machine Translation
Institute for Natural Language Processing (IMS/IfNLP)
SFB 732 - Incremental Specification in Context
Location: University of Kathmandu, see the Summer School in Advanced Language Engineering web page.
|September 18th||Part 6. Translating to morphologically rich languages: case study on German|| |
|September 17th||Part 5. Advanced topics in SMT. Discriminative bitext alignment, morphological processing, syntax|| |
Reading: Koehn 10.1, 10.2, 10.3, 11.1
|September 16th||Part 4. Log-linear Models for SMT and Minimum Error Rate Training|| powerpoint slides |
Reading: Koehn Chapter 5, 9.1, 9.2, 9.3
|September 15th||Part 3. Phrase-based Models and Decoding (automatically translating a text given an already learned model)|| powerpoint slides |
Reading: Koehn 5.1, 5.2, Chapter 6
|September 13th||Part 2. Bitext alignment (extracting lexical knowledge from parallel corpora)|| powerpoint slides |
Reading: Koehn Chapter 4
Optional Reading: Kevin Knight's SMT Tutorial (concentrate on Model 1)
|September 10th to 11th||Part 1. Introduction, basics of statistical machine translation (SMT), evaluation of MT (I also switched to slides on BLEU from Chris Callison-Burch)|| powerpoint slides |
Reading: Koehn Chapters 1 and 3
OmegaT translation memory