• Goldberg & Orwant: A Dataset of Syntactic-Ngrams over Time from a Very Large Corpus of English Books, 2013
• Yuan Zhang et al.: Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees, 2014
• Kong & Smith: An Empirical Comparison of Parsing Methods for Stanford Dependencies, 2014
• Ballesteros & Nivre: Malt Optimizer: Fast and effective parser optimization, 2014
• 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

• Marneffe, Manning: The Stanford typed dependencies representation (Rudolf Rosa)
  • (accompanied by Stanford typed dependencies manual)
• McDonald and other Google people: Universal dependency annotation for multilingual parsing (Rudolf Rosa)
  • related: Petrov et al: A universal part-of-speech tagset
• HamleDT papers (Interset, HamleDT, coordinations)

• 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: Ivan Titov or Hal Daumé have nice materials (Hal has many more).
  • 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

• Malte Nuhn, Arne Mauser, Hermann Ney: Deciphering Foreign Language by Combining Language Models and Context Vectors, 2012.
• 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.

• 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).
  1. 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.
  2. Nicolas Stroppa, Karolina Owczarzak, Andy Way: A Cluster-Based Representation for Multi-System MT Evaluation, 2007.
  3. Philipp Koehn: Statistical significance tests for machine translation evaluation, EMNLP 2004.
  4. 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
• 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.

• Yansong Feng, Mirella Lapata: Topic Models for Image Annotation and Text Illustration
• Farhadi, Hejrati, Sadeghi, Young: Every Picture Tells a Story: Generating Sentences from Images
• Kojima, Tamura: Natural Language Description of Human Activities from Video Images Based on Concept Hierarchy of Actions
• Rohrbach, Regneri et al.: Script Data for Attribute-based Recognition of Composite Activities

(TODO add some papers here )

• 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)
• Petrovic, Mathews: Unsupervised joke generation from big data (Rudolf Rosa)

• Machine Learning RG: Large Data Stuff
• Machine Learning RG: Structured prediction
• Machine Learning RG: Semisupervised and Active Learning
• Edinburgh Reading Group,
• ACL archive,I recommend trying the ACL Searchbench
• http://scholar.google.com