Dallas Card

Updates

• May 2024: I will be attending NAACL 2024 in Mexico City, where I am one of the organizers of the NLP+CSS workshop.
• April 2024: Our paper on An Archival Perspective on Pretraining Data has now been published (open access) in Patterns!
• March 2024: I will be speaking at the Emory Department of Quantitative Theory & Methods speaker series on March 25th.
• February 2024: Our PNAS paper on immigration was covered by today's Washington Post (paywalled).
• December 2023: I will be attending NeurIPS in New Orleans to present our poster, An Archival Perspective on Pretraining Data, at the SoLaR workshop on December 16th
• October 2023: Our new paper on media storms has been accepted to Findings of EMNLP
• October 2023: I will be attending the Workshop on Operationalizing the Measure Function of the NIST AI RMF in Washington, October 16-17th
• July 2023: I will be speaking about evaluation challenges at the MIDAS workshop on Generative AI for Research, July 25-26th
• July 2023: I will be attending the CASMI workshop on Sociotechnical Approaches to Measurement and Validation for Safety in AI, July 18-19th
• July 2023: I will be attending ACL 2023 in Toronto, July 9-14th, where I will be presenting a paper on Semantic Change Detection
• June 2023: I will be attending FAccT 2023 in Chicago, June 12-15th

Current Ph.D. Students

Selected Publications

• An Archival Perspective on Pretraining Data
  Meera A. Desai, Irene V. Pasquetto, Abigail Z. Jacobs, Dallas Card
  Patterns, March 2024
  [bib]


• When it Rains, it Pours: Modeling Media Storms and the News Ecosystem
  Benjamin Litterer, David Jurgens, Dallas Card
  Findings of Empirical Methods in Natural Language Processing (EMNLP), 2023
  [code] [bib]


• Substitution-based Semantic Change Detection using Contextual Embeddings
  Dallas Card
  Association for Computational Linguistics (ACL), 2023
  [code] [bib]


• Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection
  Suchin Gururangan, Dallas Card, Sarah K. Dreier, Emily K. Gade, Leroy Z. Wang, Zeyu Wang, Luke Zettlemoyer, Noah A. Smith
  Empirical Methods in Natural Language Processing (EMNLP), 2022
  [code] [bib]


• Computational analysis of 140 years of US political speeches reveals more positive but increasingly polarized framing of immigration
  Dallas Card, Serina Chang, Chris Becker, Julia Mendelsohn, Rob Voigt, Leah Boustan, Ran Abramitzky, Dan Jurafsky
  Proceedings of the National Academy of Sciences 119(31), 2022
  [data and code] [bib] Media coverage: New York Times, Washington Post


• The Values Encoded in Machine Learning Research
  Abeba Birhane, Pratyusha Kalluri, Dallas Card, William Agnew, Ravit Dotan, and Michelle Bao
  ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022
  [data and code] [bib]
  [Distinguished Paper Award]
  


• Modular Domain Adaptation
  Junshen Chen, Dallas Card, Dan Jurafsky
  In Findings of the Association of Computational Linguistics (ACL), 2022
  [blog post] [code] [bib]
  


• Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words
  Kaitlyn Zhou, Kawin Ethayarajh, Dallas Card, Dan Jurafsky
  Association for Computational Linguistics (ACL), 2022
  [bib]
  


• On the Opportunities and Risks of Foundation Models
  Rishi Bommasani, Drew A. Hudson, Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, et al.
  arXiv:2108.07258, 2021
  [bib]
  


• Expected Validation Performance and Estimation of a Random Variable's Maximum
  Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, Noah A. Smith
  Findings of Empirical Methods in Natural Language Processing (EMNLP), 2021
  [bib]
  


• Causal Effects of Linguistic Properties
  Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, and Dhanya Sridhar
  North American Chapter of the Association for Computational Linguistics (NAACL), 2021
  [bib]
  


• With Little Power Comes Great Responsibility
  Dallas Card, Peter Henderson, Urvashi Khandelwal, Robin Jia, Kyle Mahowald, and Dan Jurafsky
  Empirical Methods in Natural Language Processing (EMNLP), 2020
  [code] [bib]
  


• Detecting Stance in Media On Global Warming
  Yiwei Luo, Dallas Card, and Dan Jurafsky
  Findings of Empirical Methods in Natural Language Processing (EMNLP), 2020
  [code] [bib]
  


• Explain like I am a Scientist: The Linguistic Barriers of Entry to r/science
  Tal August, Dallas Card, Gary Hsieh, Noah A. Smith, and Katharina Reinecke
  Human Factors in Computing Systems (CHI), 2020
  [bib]
  


• On Consequentialism and Fairness
  Dallas Card and Noah A. Smith
  Frontiers in Artificial Intelligence, 2020
  [bib]
  


• Show Your Work: Improved Reporting of Experimental Results
  Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, and Noah A. Smith
  Empirical Methods in Natural Language Processing (EMNLP), 2019
  [code] [bib] Media coverage: WIRED


• Variational Pretraining for Semi-supervised Text Classification
  Suchin Gururangan, Tam Dang, Dallas Card, and Noah A. Smith
  Association for Computational Linguistics (ACL), 2019
  [code] [bib]
  


• The Risk of Racial Bias in Hate Speech Detection
  Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A. Smith
  Association for Computational Linguistics (ACL), 2019
  [bib] Media coverage: VOX
  


• Deep Weighted Averaging Classifiers
  Dallas Card, Michael Zhang, and Noah A. Smith
  ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2019
  [code] [blog post] [bib]
  


• Neural Models for Documents with Metadata
  Dallas Card, Chenhao Tan, and Noah A. Smith
  Association for Computational Linguistics (ACL), 2018
  [code] [tutorial] [bib]
  


• Friendships, Rivalries, and Trysts: Characterizing Relations between Ideas in Texts
  Chenhao Tan, Dallas Card, and Noah A. Smith
  Association for Computational Linguistics (ACL), 2017
  [blog post] [bib]
  


• Analyzing Framing through the Casts of Characters in the News
  Dallas Card, Justin H. Gross, Amber E. Boydstun, and Noah A. Smith
  Empirical Methods in Natural Language Processing (EMNLP), 2016
  [bib]
  


• The Media Frames Corpus: Annotations of Frames Across Issues
  Dallas Card, Amber E. Boydstun, Justin H. Gross, Philip Resnik, and Noah A. Smith
  Association for Computational Linguistics (ACL), 2015
  [bib]
  

Recent Professional Service

About me