Lecture 21: Statistical Machine Translation (2/2) (8/6/12)

IBM models for word alignment. Many-to-one and many-to-many alignments. IBM model 1 and the HMM alignment model. Training the alignment models: the Expectation Maximization (EM) algorithm. Symmetrizing alignments for phrase-based MT: symmetrizing by intersection; the growing heuristic. Calculating the phrase translation table. Decoding: stack decoding. Evaluation of MT systems. BLEU. Log-linear models for MT.

Lecture 20: Statistical Machine Translation (1/2) (5/6/12)

Introduction to Machine Translation. Rule-based vs. Statistical MT. Statistical MT: the noisy channel model. The language model and the translation model. The phrase-based translation model. Learning a model of training. Phrase-translation tables. Parallel corpora. Extracting phrases from word alignments. Word alignments.

Lecture 19: computational discourse (prof. Bonnie Webber)

Discourse structures and language technologies: computational discourse. What is discourse? Discourse and sentence sequences. Discourse and language features. Discourse structures. Current computational discourse modelling. Topic structure and segmentation. Functional structure and segmentation. Examples. (Lexicalized) Coherence relations. Other lexicalizations of discourse relations. No discourse relations. Discourse-enhanced Statistical Machine Translation.