NLP Reading Group
The target audience is all the members of the NLP group and other possible interested participants.
The meeting will take place weekly for one hour usually on Tuesdays from 11-12pm.
The meetings of the group will be informal and no necessary preparation will be required with the exception of the
moderator reading the current paper and the rest having at least a brief overview of it.
Next MeetingTuesday 13 December 2016
Optimization and Sampling for NLP from a Unified Viewpoint
Marc Dymetman, Guillaume Bouchard, Simon Carter
Past MeetingsTuesday 6 December 2016
Matrix Completion has No Spurious Local Minimum
Rong Ge, Jason D. Lee, Tengyu Ma
Tuesday 29 November 2016
Compositional Semantic Parsing on Semi-Structured Tables
Panupong Pasupat and Percy Liang
Tuesday 22 November 2016
Minimum Risk Training for Neural Machine Translation
Shiqi Shen, Yong Cheng, Zhougjun He, Wei He, Hua Wu, Maosong Sun, Yang Liu
Tuesday 15 November 2016
Generation from Abstract Meaning Representation using Tree Transducers
Jeffrey Flanigan, Chris Dyer, Noah A. Smith and Jaime Carbonell
Tuesday 1 November 2016
Visual Representations for Topic Understanding and Their Effects on Manually Generated Labels
Transactions of the Association for Computational Linguistics, 2016.
Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Leah Findlater, Jordan Boyd-Graber, and Niklas Elmqvist
Tuesday 25 October 2016
Chang et al. ICML 2015
Tuesday 11 October 2016
A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
Danqi Chen, Jason Bolton, Christopher D. Manning
Tuesday 4 October 2016
Ultradense Word Embeddings by Orthogonal Transformation
Sascha Rothe, Sebastian Ebert, Hinrich Schütze
Tuesday 7 June 2016
Not All Character N-grams Are Created Equal: A Study in Authorship Attribution.
Upendra Sapkota, Steven Bethard, Manuel Montes-y-Gómez & Thamar Solorio (2015)
Tuesday 31 May 2016
Relation extraction with matrix factorization and universal schemas.
Riedel, S., Yao, L., McCallum, A., & Marlin, B. M. (2013)
Tuesday 10 May 2016
Goldberg, Y. and Nivre, J. (2013)
Tuesday 3 May 2016
A New Corpus and Imitation Learning Framework for Context-Dependent Semantic Parsing
Vlachos, A. and Clark, S.
Tuesday 22 April 2016
Sequence Level Training with recurrent Neural Networks
Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba
Tuesday 22 March 2016
"Distributed Representation of Sentences and Documents"
Quoc Le and Tomas Mikolov
Tuesday 8 March 2016
AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes
Sascha Rothe; Hinrich Schütze. ACL2015 (best student paper)
Tuesday 23 February 2016
From Word Embeddings To Document Distances
Kusner et al.
Tuesday 16 February 2016
Tuesday 9 February 2016
Tuesday 25 January 2016
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks
Hua He, Kevin Gimpel, and Jimmy Lin. EMNLP2015
Tuesday 19 January 2016
Tuesday 12 January 2016
Tuesday 8 December 2015
Using Discourse Structure Improves Machine Translation Evaluation.
F Guzmán, S Joty, L Màrquez, P Nakov
And here are the author's slides
Tuesday 1 December 2015
Practical Bayesian Optimization of Machine Learning Algorithms Advances in Neural Information Processing Systems, 2012
Snoek, J.; Larochelle, H. & Adams, R. P.
Related presentations/lecture slides:
My reading group presentation slides
Tuesday 24 November 2015
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks ACL 2015
LSTMs? Kai Sheng Tai, Richard Socher, Christopher D. Manning
Additional resource about LSTM: "Anyone Can Learn To Code an LSTM-RNN in Python"
Tuesday 17 November 2015
More details on auto encoders for unsupervised pre-training:
Tuesday 10 November 2015
Tuesday 3 November 2015
Tuesday 27 October 2015
might help to read this NLP primer
a thorough explanation of back propagation
Tuesday 20 October 2015
Teaching Machines to Read and Comprehend. NIPS 2015.
Karl Moritz Hermann, Tomáš Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom
Slides (presented at LXMLS)
Tuesday 13 October 2015
A large annotated corpus for learning natural language inference. Proceedings of EMNLP 2015.
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning
Should compare this to work on (multilingual) textual similarity