Christopher D. Manning

Get To The Point: Summarization with Pointer-Generator Networks

Abigail See, Peter J. Liu, Christopher D. Manning

Comments Add METEOR evaluation results, add some citations, fix some equations (what are now equations 1, 8 and 11 were missing a bias term), fix url to pyrouge package, add acknowledgments

Deep Biaffine Attention for Neural Dependency Parsing

Timothy Dozat, Christopher D. Manning

Comments Accepted to ICLR 2017; updated with new results and comparison to more recent models, including current state-of-the-art

SceneSeer: 3D Scene Design with Natural Language

Angel X. Chang, Mihail Eric, Manolis Savva, Christopher D. Manning

Deep Reinforcement Learning for Mention-Ranking Coreference Models

Kevin Clark, Christopher D. Manning

Comments To appear in EMNLP 2016

A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task

Danqi Chen, Jason Bolton, Christopher D. Manning

Comments ACL 2016, updated results

A Fast Unified Model for Parsing and Sentence Understanding

Samuel R. Bowman, Jon Gauthier, Abhinav Rastogi, Raghav Gupta, Christopher D. Manning, Christopher Potts

Comments To appear at ACL 2016

Compression of Neural Machine Translation Models via Pruning

Abigail See, Minh-Thang Luong, Christopher D. Manning

Comments Accepted to CoNLL 2016. 9 pages plus references

Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models

Minh-Thang Luong, Christopher D. Manning

Comments 11pages, 4 figures. ACL 2016 camera-ready version. SOTA WMT'15 English-Czech 20.7 BLEU (+2.1-11.4 points)

Improving Coreference Resolution by Learning Entity-Level Distributed Representations

Kevin Clark, Christopher D. Manning

Comments Accepted for publication at the Association for Computational Linguistics (ACL), 2016

Learning Language Games through Interaction

Sida I. Wang, Percy Liang, Christopher D. Manning

Comments 11 pages, ACL 2016

Evaluating the word-expert approach for Named-Entity Disambiguation

Angel X. Chang, Valentin I. Spitkovsky, Christopher D. Manning, Eneko Agirre

Tree-structured composition in neural networks without tree-structured architectures

Samuel R. Bowman, Christopher D. Manning, Christopher Potts

Comments To appear in the proceedings of the 2015 NIPS Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches

Effective Approaches to Attention-based Neural Machine Translation

Minh-Thang Luong, Hieu Pham, Christopher D. Manning

Comments 11 pages, 7 figures, EMNLP 2015 camera-ready version, more training details

A large annotated corpus for learning natural language inference

Samuel R. Bowman, Gabor Angeli, Christopher Potts, Christopher D. Manning

Comments To appear at EMNLP 2015. The data will be posted shortly before the conference (the week of 14 Sep) at http://nlp.stanford.edu/projects/snli/

Robust Subgraph Generation Improves Abstract Meaning Representation Parsing

Keenon Werling, Gabor Angeli, Christopher Manning

Comments To appear in ACL 2015

Text to 3D Scene Generation with Rich Lexical Grounding

Angel Chang, Will Monroe, Manolis Savva, Christopher Potts, Christopher D. Manning

Comments 10 pages, 7 figures, 3 tables. To appear in ACL-IJCNLP 2015

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

Kai Sheng Tai, Richard Socher, Christopher D. Manning

Comments Accepted for publication at ACL 2015

Recursive Neural Networks Can Learn Logical Semantics

Samuel R. Bowman, Christopher Potts, Christopher D. Manning

Learning Distributed Word Representations for Natural Logic Reasoning

Samuel R. Bowman, Christopher Potts, Christopher D. Manning

Robust Logistic Regression using Shift Parameters (Long Version)

Julie Tibshirani, Christopher D. Manning

Relaxations for inference in restricted Boltzmann machines

Sida I. Wang, Roy Frostig, Percy Liang, Christopher D. Manning

Comments ICLR 2014 workshop track submission

Cross-lingual Pseudo-Projected Expectation Regularization for Weakly Supervised Learning

Mengqiu Wang, Christopher D. Manning

Zero-Shot Learning Through Cross-Modal Transfer

Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher D. Manning, Andrew Y. Ng

Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors

Danqi Chen, Richard Socher, Christopher D. Manning, Andrew Y. Ng

Probabilistic Parsing Using Left Corner Language Models

Christopher D. Manning, Bob Carpenter

Comments 12 pages, uses iwpt97.sty

Journal ref Proceedings of the Fifth International Workshop on Parsing Technologies, MIT, Boston MA, 1997