World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
21730
World Ranking
6979
National Ranking
3056

Overview

Kevin Gimpel is affiliated with the Toyota Technological Institute at Chicago in the United States. The primary discipline of their work lies within Computer Science, with a significant focus on Artificial Intelligence. Other subfields include Computer Vision and Pattern Recognition, Economics and Econometrics, Information Systems, and Signal Processing.

The body of research produced by Kevin Gimpel includes contributions to various topics, primarily centered on Topic Modeling and Natural Language Processing Techniques. Additional research interests include Text Readability and Simplification, Speech Recognition and Synthesis, Multimodal Machine Learning Applications, Advanced Text Analysis Techniques, and Text and Document Classification Technologies.

Recent publications by Kevin Gimpel encompass several peer-reviewed venues and preprint archives. Some of the notable papers include:

  • "A baseline for detecting misclassified and out-of-distribution examples in neural networks," 2025, published in arXiv (Cornell University)
  • "SummScreen: A Dataset for Abstractive Screenplay Summarization," 2022, presented at the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • "SummScreen: A Dataset for Abstractive Screenplay Summarization," 2021, archived in arXiv (Cornell University)
  • "Chess as a Testbed for Language Model State Tracking," 2022, included in the Proceedings of the AAAI Conference on Artificial Intelligence
  • "How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions," 2020, also part of the Proceedings of the AAAI Conference on Artificial Intelligence

Kevin Gimpel has frequently collaborated with a range of researchers, with several repeat co-authorships. Among the most frequent collaborators are:

  • Karen Livescu
  • Mingda Chen
  • Zewei Chu
  • Sam Wiseman
  • Haoyue Shi

Their work has been disseminated through multiple key publication venues. The majority of their papers appear in arXiv (Cornell University), contributing to 27 publications. Additional venues include the Proceedings of the AAAI Conference on Artificial Intelligence with 3 publications, and the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) with 2 publications.

Best Publications

  • ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

    Zhenzhong Lan;Mingda Chen;Sebastian Goodman;Kevin Gimpel

  • Gaussian Error Linear Units (GELUs)

    Dan Hendrycks;Kevin Gimpel

  • A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

    Dan Hendrycks;Kevin Gimpel

  • Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments

    Kevin Gimpel;Nathan Schneider;Brendan O'Connor;Dipanjan Das

  • Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units

    Dan Hendrycks;Kevin Gimpel

  • Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters

    Olutobi Owoputi;Brendan O'Connor;Chris Dyer;Kevin Gimpel

  • ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

    Zhenzhong Lan;Mingda Chen;Sebastian Goodman;Kevin Gimpel

  • Adversarial Example Generation with Syntactically Controlled Paraphrase Networks

    Mohit Iyyer;John Wieting;Kevin Gimpel;Luke Zettlemoyer

  • Towards Universal Paraphrastic Sentence Embeddings

    John Wieting;Mohit Bansal;Kevin Gimpel;Karen Livescu

  • Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks

    Hua He;Kevin Gimpel;Jimmy Lin

  • Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise

    Dan Hendrycks;Mantas Mazeika;Duncan Wilson;Kevin Gimpel

  • Tailoring Continuous Word Representations for Dependency Parsing

    Mohit Bansal;Kevin Gimpel;Karen Livescu

  • ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations

    John Wieting;Kevin Gimpel

  • From Paraphrase Database to Compositional Paraphrase Model and Back

    John Wieting;Mohit Bansal;Kevin Gimpel;Karen Livescu

  • Movie Reviews and Revenues: An Experiment in Text Regression

    Mahesh Joshi;Dipanjan Das;Kevin Gimpel;Noah A. Smith

  • Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics

    Kevin Gimpel;Nathan Schneider;Brendan O'Connor;Dipanjan Das

  • Charagram: Embedding Words and Sentences via Character n-grams

    John Wieting;Mohit Bansal;Kevin Gimpel;Karen Livescu

  • Commonsense Knowledge Base Completion

    Xiang Li;Aynaz Taheri;Lifu Tu;Kevin Gimpel

  • Deep Multilingual Correlation for Improved Word Embeddings

    Ang Lu;Weiran Wang;Mohit Bansal;Kevin Gimpel

  • Beyond BLEU: Training Neural Machine Translation with Semantic Similarity.

    John Wieting;Taylor Berg-Kirkpatrick;Kevin Gimpel;Graham Neubig

  • Softmax-Margin CRFs: Training Log-Linear Models with Cost Functions

    Kevin Gimpel;Noah A. Smith

Frequent Co-Authors

Karen Livescu
Karen Livescu Toyota Technological Institute at Chicago
Noah A. Smith
Noah A. Smith University of Washington
Mohit Bansal
Mohit Bansal University of North Carolina at Chapel Hill
David McAllester
David McAllester Toyota Technological Institute at Chicago
Chris Dyer
Chris Dyer Google (United States)
Graham Neubig
Graham Neubig Carnegie Mellon University
Taylor Berg-Kirkpatrick
Taylor Berg-Kirkpatrick University of California, San Diego
Dipanjan Das
Dipanjan Das Google (United States)
Mari Ostendorf
Mari Ostendorf University of Washington
Luke Zettlemoyer
Luke Zettlemoyer University of Washington

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online education in Computer Science opens up diverse career routes and flexible study options. Many students now consider the most affordable online MBA programs to build business acumen alongside technical expertise. This blend can enhance leadership skills and open doors to tech management roles.

For those who want to fast-track their education, a 1 year masters program can be a smart choice. These accelerated pathways allow you to quickly gain advanced skills, helping you enter the workforce or qualify for promotions sooner.

Job seekers interested in maximizing their return on investment may research the best online degrees that often lead to high-paying roles in a short time frame. Options include programs in information technology, data science, and cybersecurity.

If you’re passionate about cutting-edge technology, pursuing an artificial intelligence degree online can put you at the forefront of innovation. These specialized programs prepare graduates for exciting careers in AI, machine learning, and automation—fields consistently in high demand.

Best Scientists Citing Kevin Gimpel

Trending Scientists