World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
68
Citations
27431
World Ranking
2036
National Ranking
1030

Research.com Recognitions

  • 2014 - Fellow of Alfred P. Sloan Foundation

Overview

Chris Callison-Burch is affiliated with the University of Pennsylvania in the United States. Their research primarily spans the field of Computer Science, with a focus on Artificial Intelligence and Computer Vision and Pattern Recognition, alongside interdisciplinary interests in General Health Professions, Sociology and Political Science, and Molecular Biology.

Their scholarly output covers a wide range of topics, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Text Readability and Simplification
  • Explainable Artificial Intelligence (XAI)
  • Machine Learning in Healthcare
  • Speech Recognition and Synthesis

Callison-Burch has contributed extensively to various top-tier publication venues with multiple papers appearing in:

  • arXiv (Cornell University) with 82 publications
  • Proceedings of the AAAI Conference on Artificial Intelligence with 3 publications
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing with 3 publications
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) with 2 publications
  • PLoS ONE with 2 publications

Recent notable papers include:

  • "Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models" (2022, arXiv)
  • "Deduplicating Training Data Makes Language Models Better" (2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics - Long Papers)
  • "Artificial Intelligence in mental health and the biases of language based models" (2020, PLoS ONE)
  • "Towards Faithful Model Explanation in NLP: A Survey" (2024, Computational Linguistics)
  • "Real or Fake Text?: Investigating Human Ability to Detect Boundaries between Human-Written and Machine-Generated Text" (2023, Proceedings of the AAAI Conference on Artificial Intelligence)

Frequent co-authors working alongside Callison-Burch include:

  • Liam Dugan
  • Daphne Ippolito
  • Marianna Apidianaki
  • Mark Yatskar
  • Andrew Zhu

Callison-Burch was awarded the title of Fellow of the Alfred P. Sloan Foundation in 2014.

Best Publications

  • Moses: Open Source Toolkit for Statistical Machine Translation

    Philipp Koehn;Hieu Hoang;Alexandra Birch;Chris Callison-Burch

  • Re-evaluating the Role of Bleu in Machine Translation Research

    Chris Callison-Burch;Miles Osborne;Philipp Koehn

  • PPDB: The Paraphrase Database

    Juri Ganitkevitch;Benjamin Van Durme;Chris Callison-Burch

  • Findings of the 2012 Workshop on Statistical Machine Translation

    Chris Callison-Burch;Philipp Koehn;Christof Monz;Matt Post

  • Findings of the 2009 Workshop on Statistical Machine Translation

    Chris Callison-Burch;Philipp Koehn;Christof Monz;Josh Schroeder

  • Paraphrasing with Bilingual Parallel Corpora

    Colin Bannard;Chris Callison-Burch

  • Method and apparatus for providing multilingual translation over a network

    Christopher Callison-Burch;Jeffrey Chin;Raymond Flournoy;Pria Hidisyan

  • Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon's Mechanical Turk

    Chris Callison-Burch

  • A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk

    Kotaro Hara;Abigail Adams;Kristy Milland;Saiph Savage

  • Optimizing Statistical Machine Translation for Text Simplification

    Wei Xu;Courtney Napoles;Ellie Pavlick;Quanze Chen

  • Crowdsourcing Translation: Professional Quality from Non-Professionals

    Omar F. Zaidan;Chris Callison-Burch

  • Findings of the 2013 Workshop on Statistical Machine Translation

    Ondřej Bojar;Christian Buck;Chris Callison-Burch;Christian Federmann

  • Findings of the 2011 Workshop on Statistical Machine Translation

    Chris Callison-Burch;Philipp Koehn;Christof Monz;Omar Zaidan

  • Edinburgh System Description for the 2005 IWSLT Speech Translation Evaluation

    Philipp Koehn;Amittai Axelrod;Alexandra Birch-Mayne;Chris Callison-Burch

  • Creating Speech and Language Data With Amazon's Mechanical Turk

    Chris Callison-Burch;Mark Dredze

  • Problems in Current Text Simplification Research: New Data Can Help

    Wei Xu;Chris Callison-Burch;Courtney Napoles

  • (Meta-) Evaluation of Machine Translation

    Chris Callison-Burch;Cameron Fordyce;Philipp Koehn;Christof Monz

  • Arabic dialect identification

    Omar F. Zaidan;Chris Callison-Burch

  • Improved Statistical Machine Translation Using Paraphrases

    Chris Callison-Burch;Philipp Koehn;Miles Osborne

  • PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification

    Ellie Pavlick;Pushpendre Rastogi;Juri Ganitkevitch;Benjamin Van Durme

  • Open Source Toolkit for Statistical Machine Translation: Factored Translation Models and Lattice Decoding

    Philipp Koehn;Marcello Federico;Wade Shen;Nicola Bertoldi

Frequent Co-Authors

Philipp Koehn
Philipp Koehn Johns Hopkins University
Christof Monz
Christof Monz University of Amsterdam
Benjamin Van Durme
Benjamin Van Durme Johns Hopkins University
Miles Osborne
Miles Osborne Bloomberg LP
Sanjeev Khudanpur
Sanjeev Khudanpur Johns Hopkins University
Yuan Cao
Yuan Cao Google (United States)
Mark Dredze
Mark Dredze Johns Hopkins University
Douglas Eck
Douglas Eck Google (United States)
Chris Dyer
Chris Dyer Google (United States)
Kenji Sagae
Kenji Sagae University of California, Davis

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