Listed by year below; also see my profiles on Google Scholar and Semantic Scholar.

2017

End-to-End Neural Segmental Models for Speech Recognition
Hao Tang, Liang Lu, Lingpeng Kong, Kevin Gimpel, Karen Livescu, Chris Dyer, Noah A. Smith, and Steve Renals
IEEE Journal of Selected Topics in Signal Processing
[arxiv]

Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext
John Wieting, Jonathan Mallinson, and Kevin Gimpel
EMNLP 2017
[arxiv] [bib]

Learning to Embed Words in Context for Syntactic Tasks
Lifu Tu, Kevin Gimpel, and Karen Livescu
2nd Workshop on Representation Learning for NLP  (best paper award)
[arxiv] [bib]

Emergent Predication Structure in Hidden State Vectors of Neural Readers
Hai Wang, Takeshi Onishi, Kevin Gimpel, and David McAllester
2nd Workshop on Representation Learning for NLP  (best paper award)
[arxiv] [bib]

Joint Modeling of Text and Acoustic-Prosodic Cues for Neural Parsing
Trang Tran, Shubham Toshniwal, Mohit Bansal, Kevin Gimpel, Karen Livescu, and Mari Ostendorf
[arxiv]

Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings
John Wieting and Kevin Gimpel
ACL 2017
[arxiv] [code] [bib]

Pay Attention to the Ending: Strong Neural Baselines for the ROC Story Cloze Task
Zheng Cai, Lifu Tu, and Kevin Gimpel
ACL 2017
[paper] [bib]

A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Dan Hendrycks and Kevin Gimpel
ICLR 2017
[arxiv] [bib]

Early Methods for Detecting Adversarial Images
Dan Hendrycks and Kevin Gimpel
ICLR 2017 (workshop contribution)
[arxiv] [bib]

Broad Context Language Modeling as Reading Comprehension
Zewei Chu, Hai Wang, Kevin Gimpel, and David McAllester
EACL 2017
[arxiv] [slides] [training data (330MB)] [manual analysis] [bib]


2016

Constraints Based Convex Belief Propagation
Yaniv Tenzer, Alexander Schwing, Kevin Gimpel, and Tamir Hazan
NIPS 2016
[paper] [bib]

End-to-End Training Approaches for Discriminative Segmental Models
Hao Tang, Weiran Wang, Kevin Gimpel, and Karen Livescu
SLT 2016
[arxiv] [bib]

Charagram: Embedding Words and Sentences via Character n-grams
John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu
EMNLP 2016
[arxiv] [code and models] [bib]

Who did What: A Large-Scale Person-Centered Cloze Dataset
Takeshi Onishi, Hai Wang, Mohit Bansal, Kevin Gimpel and David McAllester
EMNLP 2016
[arxiv] [data] [bib]

Adjusting for Dropout Variance in Batch Normalization and Weight Initialization
Dan Hendrycks and Kevin Gimpel
[arxiv]

Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units
Dan Hendrycks and Kevin Gimpel
[arxiv]

Efficient Segmental Cascades for Speech Recognition
Hao Tang, Weiran Wang, Kevin Gimpel, and Karen Livescu
Interspeech 2016
[arxiv] [bib]

Mapping Unseen Words to Task-Trained Embedding Spaces
Pranava Swaroop Madhyastha, Mohit Bansal, Kevin Gimpel, and Karen Livescu
1st Workshop on Representation Learning for NLP  (best paper award)
[arxiv] [bib]

Commonsense Knowledge Base Completion
Xiang Li, Aynaz Taheri, Lifu Tu, and Kevin Gimpel
ACL 2016
[paper] [resources] [bib]

UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks
for Textual Similarity Measurement

Hua He, John Wieting, Kevin Gimpel, Jinfeng Rao, Jimmy Lin
SemEval 2016
[paper] [bib]

Towards Universal Paraphrastic Sentence Embeddings
John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu
ICLR 2016
[arxiv] [code] [embeddings] [bib]


2015

Discriminative Segmental Cascades for Feature-Rich Phone Recognition
Hao Tang, Weiran Wang, Kevin Gimpel, and Karen Livescu
ASRU 2015  (best paper nominee)
[paper] [bib] [arxiv]

Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks
Hua He, Kevin Gimpel, and Jimmy Lin
EMNLP 2015
[paper] [poster] [code] [bib]

Machine Comprehension with Syntax, Frames, and Semantics
Hai Wang, Mohit Bansal, Kevin Gimpel, and David McAllester
ACL 2015
[paper] [bib]

From Paraphrase Database to Compositional Paraphrase Model and Back
John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu, and Dan Roth
TACL 2015 (presented at EMNLP 2015)
[paper] [embeddings/data/code] [bib]

Deep Multilingual Correlation for Improved Word Embeddings
Ang Lu, Weiran Wang, Mohit Bansal, Kevin Gimpel, and Karen Livescu
NAACL 2015
[paper] [code] [bib]

A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment
Jing Wang, Mohit Bansal, Kevin Gimpel, Brian D. Ziebart, and Clement T. Yu
TACL 2015 (presented at NAACL 2015)
[paper] [poster] [one-minute madness slide] [bib]


2014

Weakly-Supervised Learning with Cost-Augmented Contrastive Estimation
Kevin Gimpel and Mohit Bansal
EMNLP 2014
[paper] [supplementary material] [slides] [talk] [bib]

A Comparison of Training Approaches for Discriminative Segmental Models
Hao Tang, Kevin Gimpel, and Karen Livescu
Interspeech 2014
[paper] [code] [bib]

Tailoring Continuous Word Representations for Dependency Parsing
Mohit Bansal, Kevin Gimpel, and Karen Livescu
ACL 2014
[paper] [slides] [data] [bib]

Phrase Dependency Machine Translation with Quasi-Synchronous Tree-to-Tree Features
Kevin Gimpel and Noah A. Smith
Computational Linguistics
[paper] [bib]


2013

A Systematic Exploration of Diversity in Machine Translation
Kevin Gimpel, Dhruv Batra, Chris Dyer, and Gregory Shakhnarovich
EMNLP 2013
[paper] [supplementary material] [poster] [bib]

Predicting the NFL using Twitter
Shiladitya Sinha, Chris Dyer, Kevin Gimpel, and Noah A. Smith
ECML/PKDD 2013 Workshop on Machine Learning and Data Mining for Sports Analytics
[paper] [data] [bib]

Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters
Olutobi Owoputi, Brendan O'Connor, Chris Dyer, Kevin Gimpel, Nathan Schneider, and Noah A. Smith
NAACL 2013
[paper] [poster] [data and software] [bib]


2012

Discriminative Feature-Rich Modeling for Syntax-Based Machine Translation
Kevin Gimpel
Ph.D. Thesis, Language Technologies Institute, Carnegie Mellon University
[thesis] [bib]

Part-of-Speech Tagging for Twitter: Word Clusters and Other Advances
Olutobi Owoputi, Brendan O'Connor, Chris Dyer, Kevin Gimpel, and Nathan Schneider
Technical report CMU-ML-12-107
[paper] [data and software] [bib]

Word Salad: Relating Food Prices and Descriptions
Victor Chahuneau, Kevin Gimpel, Bryan R. Routledge, Lily Scherlis, and Noah A. Smith
EMNLP 2012
[paper] [supplementary material] [data: train.json.gz, dev.json.gz, test.json.gz] [bib]

Concavity and Initialization for Unsupervised Dependency Parsing
Kevin Gimpel and Noah A. Smith
NAACL 2012
[paper] [slides] [bib]

Structured Ramp Loss Minimization for Machine Translation
Kevin Gimpel and Noah A. Smith
NAACL 2012
[paper] [addendum] [poster] [code] [bib]


2011

Generative Models of Monolingual and Bilingual Gappy Patterns
Kevin Gimpel and Noah A. Smith
WMT 2011
[paper] [slides] [code] [sample patterns] [bib]

The CMU-ARK German-English Translation System
Chris Dyer, Kevin Gimpel, Jonathan H. Clark, and Noah A. Smith
WMT 2011
[paper] [bib]

Quasi-Synchronous Phrase Dependency Grammars for Machine Translation
Kevin Gimpel and Noah A. Smith
EMNLP 2011
[paper] [slides] [bib]

Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments
Kevin Gimpel, Nathan Schneider, Brendan O'Connor, Dipanjan Das, Daniel Mills, Jacob Eisenstein, Michael Heilman, Dani Yogatama, Jeffrey Flanigan, and Noah A. Smith
ACL 2011
[paper] [slides] [data and software] [bib]


2010

Learning Structured Classifiers with Dual Coordinate Ascent
André F. T. Martins, Kevin Gimpel, Noah A. Smith, Eric P. Xing, Pedro M. Q. Aguiar, and Mário A. T. Figueiredo
Technical report CMU-ML-10-109
[paper] [bib]

Distributed Asynchronous Online Learning for Natural Language Processing
Kevin Gimpel, Dipanjan Das, and Noah A. Smith
CoNLL 2010
[paper] [slides] [bib]

Softmax-Margin CRFs: Training Log-Linear Models with Cost Functions
Kevin Gimpel and Noah A. Smith
NAACL 2010
[paper] [slides] [bib]
     Extended technical report version:
     Softmax-Margin Training for Structured Log-Linear Models
     Kevin Gimpel and Noah A. Smith
     CMU-LTI-10-008
     [paper] [bib]

Movie Reviews and Revenues: An Experiment in Text Regression
Mahesh Joshi, Dipanjan Das, Kevin Gimpel, and Noah A. Smith
NAACL 2010
[paper] [poster] [data] [bib]


2009

Feature-Rich Translation by Quasi-Synchronous Lattice Parsing
Kevin Gimpel and Noah A. Smith
EMNLP 2009
[paper] [slides] [bib]

Cube Summing, Approximate Inference with Non-Local Features, and Dynamic Programming without Semirings
Kevin Gimpel and Noah A. Smith
EACL 2009
[paper] [slides] [bib]


2008

Logistic Normal Priors for Unsupervised Probabilistic Grammar Induction
Shay B. Cohen, Kevin Gimpel, and Noah A. Smith
NIPS 21
[paper] [code] [bib]

Rich Source-Side Context for Statistical Machine Translation
Kevin Gimpel and Noah A. Smith
WMT 2008  (5-year retrospective best paper award)
[paper] [code for significance testing] [bib]



Other Papers/Presentations (unpublished):

Beating the NFL Football Point Spread. Course project report for Machine Learning, 2006. If you're interested in the data I used, check out my brother's company.

Notes on graphical models

Junction Tree Algorithms for Inference in Dynamic Bayesian Networks