Statistical Machine Translation (SMT) was the dominant approach used for online translation until 2015. Neural Machine Translation (NMT) is the new dominant approach.
Neural Machine Translation (NMT) is a new paradigm in data-driven machine translation. Previous generation Statistical Machine Translation (SMT) systems are built using a collection of heuristic models, typically combined in a log-linear model with a small number of parameters. In Neural Machine Translation, the entire translation process is posed as an end-to-end supervised classification problem, where the training data is pairs of sentences. While in SMT systems, word-alignment is carried out, and then fixed, and then various sub-models are estimated from the word-aligned data, this is not the case in NMT. In NMT, fixed word-alignments are not used, and instead the full sequence to sequence task is handled in one model.
The seminar will begin with the basics of Statistical Machine Translation and then briefly introduce Deep Learning before covering the basics of Neural Machine Translation.
The goal of the seminar is to understand the basics of SMT and NMT. The varying role of the lexicon (and representations of the lexicon) in these approaches is a critical aspect which will be a focus of study.
Email Address: SubstituteMyLastName@cis.uni-muenchen.de
CIS, LMU Munich
Room U139, Tuesdays, 16:00 to 18:00 (c.t.)
|Date||Topic||Reading (DO BEFORE THE MEETING!)||Slides|
|October 18th||Introduction to Statistical Machine Translation||ppt pdf|
|October 25th||Bitext alignment (extracting lexical knowledge from parallel corpora)||ppt pdf|
|November 8th||Many-to-many alignments and Phrase-based model||ppt pdf|
|November 15th|| Log-linear model and Minimum Error Rate Training |
| ppt pdf |
|November 22nd||Decoding (Guest Lecture from Tsuyoshi Okita)|
|November 29th||Introduction to Linear Models (SLIDES UPDATED!)||pptx pdf|
|December 6th||Neural Networks (and Word Embeddings), Fabienne Braune|
|December 13th||Recurrent Neural Networks, Tsuyoshi Okita|
|December 20th||SMT: Advanced Word Alignment, Morphology, Syntax||ppt pdf|
|January 24th||Neural Machine Translation, Matthias Huck|
Referatsthemen (name: topic)
|January 10th||Palchik: Word-Sense-Disambiguation and WSD for SMT||yes|
|January 10th||Deck: Computer-Aided Translation||yes|
|January 17th||Bilan: Cross-Lingual Lexical Substitution||yes|
|January 17th||Sedinkina: Wikification of Ambiguous Entities||yes|
|January 24th||SEE ABOVE|
|January 31st||Poerner: System Combination||yes|
|January 31st||Krachenfels: Neural Parsing with Gated Recursive Convolutional Networks||yes|
Philipp Koehn's book Statistical Machine Translation
Kevin Knight's tutorial on SMT (particularly look at IBM Model 1)