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.
Here is a link to last semester's seminar.
NEW: David Kaumanns is also organizing a Munich interest group for Deep Learning, which has an associated mailing list. See the link here: http://www.cis.uni-muenchen.de/~davidk/deep-munich/
Email Address: SubstituteLastName@cis.uni-muenchen.de
CIS, LMU Munich
Thursdays 14:30 s.t., location is C105 (CIS Besprechungsraum).
Click here for directions to CIS.
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|Thursday, April 28th||Karthik Narasimhan, Tejas Kulkarni, Regina Barzilay (2015). Language Understanding for Text-based Games Using Deep Reinforcement Learning. Proceedings of EMNLP (Best paper honorable mention)||paper and slides||David Kaumanns|
|Thursday, May 19th||Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Jozefowicz, Samy Bengio (2016). Generating Sentences from a Continuous Space. arXiv preprint.||paper||Ben Roth|
|Thursday, June 9th||Rico Sennrich, Barry Haddow, Alexandra Birch (2015). Neural Machine Translation of Rare Words with Subword Units. arXiv preprint.||paper||Matthias Huck|
|Thursday, June 23rd||Junyoung Chung, Kyunghyun Cho, Yoshua Bengio (2016). A Character-level Decoder without Explicit Segmentation for Neural Machine Translation. arXiv preprint.||paper||Ales Tamchyna|
|Thursday, July 14th||Carl Doersch (2016). Tutorial on Variational Autoencoders. arXiv preprint.||paper||Yadollah Yaghoobzadeh|
|Thursday, July 21st||Yangfeng Ji, Gholamreza Haffari, Jacob Eisenstein (2016). A Latent Variable Recurrent Neural Network for Discourse Relation Language Models. NAACL 2016||paper||Liane Guillou|
|Thursday, July 28th||Presentation by Ivan Bilan on Bilan+Zhekova submission to the PAN shared task||No Reading|
|Thursday, August 18th||5 favorite papers from ACL 2016, WMT 2016, etc. (Strict 1 minute per paper)||Everyone (Please Bring a Handout)|
|Thursday, August 25th||Piotr Bojanowski, Edouard Grave, Armand Joulin, Tomas Mikolov (2016). Enriching Word Vectors with Subword Information. arXiv preprint.||paper|
|Thursday, October 6th||Dan Gillick, Cliff Brunk, Oriol Vinyals, Amarnag Subramanya (2016). Multilingual Language Processing From Bytes. HLT-NAACL 2016.||paper||Hinrich Schütze|
|Thursday, October 13th||Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, Hang Li (2016). Modeling Coverage for Neural Machine Translation. ACL 2016||paper||Tsuyoshi Okita|
Please click here for an NMT reading list, but also see the more general RNN reading list here (scroll down).