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Table of Contents
Wishlist
Parsing
- Keith Hall: k-best Spanning Tree Parsing ACL 2007
- Introduction to MALT Parser (one of the many papers by Joakim Nivre) + one advance technique, e.g. An Improved Oracle for Dependency Parsing with Online Reordering
- Koo et al.: Dual Decomposition for Parsing with Non-Projective Head Automata EMNLP 2010.
- Eugene Charniak: A maximum-entropy-inspired parser (Zdeněk Žabokrtský)
- Reut Tsarfaty, Joakim Nivre, Evelina Andersson: Cross-Framework Evaluation for Statistical Parsing, EACL 2012
Machine Learning
- Something about
SEARN, MegaM, Vowpal Wabbit and/or its applications. SEARN could be presented once again, if someone goes through the source codes. - Yoav Goldberg, Michael Elhadad: splitSVM: Fast, Space-Efficient, non-Heuristic, Polynomial Kernel Computation for NLP Applications ACL 2008
- Ryan McDonald, Keith Hall, Gideon Mann: Distributed Training Strategies for the Structured Perceptron
- Kernels and Tree kernels:
- Something about kernel methods in general (for SVM, perceptron etc.)
- M. Collins and N. Duffy: Convolution kernels for natural language, NIPS 2001. And a related paper.
- Aron Culotta, Jeffrey Sorensen: Dependency Tree Kernels for Relation Extraction
- Structured prediction:
- Introduction to structured prediction, maybe structured perceptron, see the slides at the end of Ivan Titov's course web
- Andrew McCallum, Dayne Freitag, Fernando Pereira: Maximum Entropy Markov Models for Information Extraction and Segmentation, Conference on Machine Learning 2000, slides
- John Lafferty, Andrew McCallum, Fernando Pereira: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, 2001
- Sunita Sarawagi, William Cohen: Semi-Markov conditional random fields for information extraction, Advances in Neural Information Processing Systems, 2004
Machine Translation
- Something about word alignment, recap IBM 1-5 (GIZA++), using word classes, HMM alignments. What is state of the art?
- Ann Clifton, Anoop Sarkar: Combining Morpheme-based Machine Translation with Post-processing Morpheme Prediction ACL 2011
- Taro Watanabe, Eiichiro Sumit: Machine Translation System Combination by Confusion Forest ACL 2011
- Nan Duan, Mu Li, Ming Zhou: Hypothesis Mixture Decoding for Statistical Machine Translation ACL 2011
- Abhishek Arun, Chris Dyer, Barry Haddow, Phil Blunsom, Adam Lopez and Philipp Koehn: Monte Carlo Inference and Maximization for Phrase-based Translation. Conference on Computational Natural Language Learning, 2009.
- Phil Blunsom, Trevor Cohn, Chris Dyer and Miles Osborne: A Gibbs Sampler for Phrasal Synchronous Grammar Induction. ACL-IJCNLP 2009
- Trevor Cohn and Phil Blunsom: A Bayesian Model of Syntax-Directed Tree to String Grammar Induction. EMNLP 2009.
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: 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: A Cluster-Based Representation for Multi-System MT Evaluation, 2007.
Philipp Koehn: Statistical significance tests for machine translation evaluation, EMNLP 2004.- Ying Zhang, Stephan Vogel, Alex Waibel: Interpreting BLEU/NIST Scores: How Much Improvement Do We Need to Have a Better System?
- T. Berg-Kirkpatrick, D. Burkett, D. Klein: An Empirical Investigation of Statistical Significance in NLP
- Chi-kiu LO and Dekai WU: 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)
- Joseph P. Simmons, Leif D. Nelson, Uri Simonsohn: False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant, Psychological Science, 2011. Yes, it is a psychological paper, but it is very valuable for anyone doing/reading any evaluation with significance tests.
Other
Jakob Uszkoreit, Thorsten Brants: Distributed Word Clustering for Large Scale Class-Based Language Modeling in Machine Translation ACL 2008- Helmut Schmid, Florian Laws: Estimation of Conditional Probabilities With Decision Trees and an Application to Fine-Grained POS Tagging Coling 2008
- Mark Johnson: Why Doesn't EM Find Good HMM POS-Taggers? (Ondřej Bojar)