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courses:rg:wishlist [2012/10/08 23:56]
rosa meant (doplněn konkrétní článek) |
courses:rg:wishlist [2012/10/14 22:07]
popel 4 new suggestions |
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| ==== Machine Learning ==== | ==== Machine Learning ==== |
| * Something about [[http://searn.hal3.name/|SEARN]] or [[http://www.cs.utah.edu/~hal/megam/|MegaM]] and/or its applications | * Something about <del>[[http://searn.hal3.name/|SEARN]]</del>, [[http://www.cs.utah.edu/~hal/megam/|MegaM]], [[http://hunch.net/~vw/|Vowpal Wabbit]] and/or its applications. [[courses:rg:2012:searn-in-practice|SEARN]] could be presented once again, if someone goes through the source codes. |
| * Andrew McCallum, Dayne Freitag, Fernando Pereira: [[http://www.ai.mit.edu/courses/6.891-nlp/READINGS/maxent.pdf|Maximum Entropy Markov Models for Information Extraction and Segmentation]], Conference on Machine Learning 2000, [[http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/gidofalvi.pdf|slides]] | * Andrew McCallum, Dayne Freitag, Fernando Pereira: [[http://www.ai.mit.edu/courses/6.891-nlp/READINGS/maxent.pdf|Maximum Entropy Markov Models for Information Extraction and Segmentation]], Conference on Machine Learning 2000, [[http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/gidofalvi.pdf|slides]] |
| * John Lafferty, Andrew McCallum, Fernando Pereira: [[http://www.cis.upenn.edu/~pereira/papers/crf.pdf|Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data]], 2001 | * John Lafferty, Andrew McCallum, Fernando Pereira: [[http://www.cis.upenn.edu/~pereira/papers/crf.pdf|Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data]], 2001 |
| * Phil Blunsom, Trevor Cohn, Chris Dyer and Miles Osborne: [[http://homepages.inf.ed.ac.uk/pblunsom/pubs/blunsom-acl09.pdf|A Gibbs Sampler for Phrasal Synchronous Grammar Induction. ACL-IJCNLP 2009]] | * Phil Blunsom, Trevor Cohn, Chris Dyer and Miles Osborne: [[http://homepages.inf.ed.ac.uk/pblunsom/pubs/blunsom-acl09.pdf|A Gibbs Sampler for Phrasal Synchronous Grammar Induction. ACL-IJCNLP 2009]] |
| * Trevor Cohn and Phil Blunsom: [[http://homepages.inf.ed.ac.uk/pblunsom/pubs/cohn-blunsom-emnlp09.pdf|A Bayesian Model of Syntax-Directed Tree to String Grammar Induction. EMNLP 2009.]] | * Trevor Cohn and Phil Blunsom: [[http://homepages.inf.ed.ac.uk/pblunsom/pubs/cohn-blunsom-emnlp09.pdf|A Bayesian Model of Syntax-Directed Tree to String Grammar Induction. EMNLP 2009.]] |
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| | ==== MT Evaluation ==== |
| | * Martin Popel would appreciate two RG meetings devoted to significance tests & MT evaluation. The two presenters should together read the following 4 papers (and related ones) and select two for presenting (one on bootstrap, one on approximate randomization). |
| | - Stefan Riezler and John T. Maxwell III: [[http://acl.ldc.upenn.edu/W/W05/W05-0908.pdf|On Some Pitfalls in Automatic Evaluation and Significance Testing for MT]] (page 67) ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation, 2005. |
| | - Nicolas Stroppa, Karolina Owczarzak, Andy Way: [[http://doras.dcu.ie/15227/1/stroppa_owczarzak_07.pdf|A Cluster-Based Representation for Multi-System MT Evaluation]], 2007. |
| | - Philipp Koehn: [[http://acl.ldc.upenn.edu/acl2004/emnlp/pdf/Koehn.pdf|Statistical significance tests for machine translation evaluation]], EMNLP 2004. |
| | - Ying Zhang, Stephan Vogel, Alex Waibel: [[http://www.lrec-conf.org/proceedings/lrec2004/pdf/755.pdf|Interpreting BLEU/NIST Scores: How Much Improvement Do We Need to Have a Better System?]] |
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| * Chi-kiu LO and Dekai WU: [[http://www.cs.ust.hk/~dekai/library/WU_Dekai/LoWu_Acl2011.pdf|MEANT: An inexpensive, high-accuracy, semi-automatic metric for evaluating translation utility via semantic frames. ACL HLT 2011]] (or other MEANT or HMEANT paper, but this one seems to be THE main one) | * Chi-kiu LO and Dekai WU: [[http://www.cs.ust.hk/~dekai/library/WU_Dekai/LoWu_Acl2011.pdf|MEANT: An inexpensive, high-accuracy, semi-automatic metric for evaluating translation utility via semantic frames. ACL HLT 2011]] (or other MEANT or HMEANT paper, but this one seems to be THE main one) |
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