I am a postdoctoral associate at the New York University Center for Data Science, working with Tal Linzen. I received my PhD in Computational Linguistics from the Ohio State University, advised by William Schuler. My research uses broad-coverage computational models to investigate questions related to human sentence processing, language acquisition, and memory.

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Publications

• Associative Memory Updates in Language Models Predict Human Reading Times.
  Christian Clark and William Schuler.
  Findings of the Association for Computational Linguistics: EMNLP 2026, 2026.
  [paper] [code] [bibtex]
  [copy]

  @inproceedings{clarkschuler2026associative,
      author = "Clark, Christian and Schuler, William",
      title = "Associative Memory Updates in Language Models Predict Human Reading Times",
      booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2026",
      year = "2026"
  }

• Evaluating Humanlike Memory Effects in Transformers Using Item Recognition Tasks.
  Christian Clark and William Schuler.
  30th Conference on Computational Natural Language Learning, 2026.
  [paper] [code] [bibtex]
  [copy]

  @inproceedings{clarkschuler2026evaluating,
      author = "Clark, Christian and Schuler, William",
      title = "Evaluating Humanlike Memory Effects in Transformers Using Item Recognition Tasks",
      booktitle = "30th Conference on Computational Natural Language Learning",
      year = "2026"
  }

• How Well Does First-Token Entropy Approximate Word Entropy as a Psycholinguistic Predictor?
  Christian Clark, Byung-Doh Oh, and William Schuler.
  Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, 2025.
  [paper] [code] [bibtex]
  [copy]

  @inproceedings{clarketal2025entropy,
      author = "Clark, Christian and Oh, Byung-Doh and Schuler, William",
      title = "How Well Does First-Token Entropy Approximate Word Entropy as a Psycholinguistic Predictor?",
      booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
      pages = "47--57",
      year = "2025"
  }

• Linear Recency Bias During Training Improves Transformers’ Fit to Reading Times.
  Christian Clark, Byung-Doh Oh, and William Schuler.
  Proceedings of the 31st International Conference on Computational Linguistics, 2025.
  [paper] [code] [bibtex]
  [copy]

  @inproceedings{clarketal2025linear,
      author = "Clark, Christian and Oh, Byung-Doh and Schuler, William",
      title = "Linear Recency Bias During Training Improves Transformers’ Fit to Reading Times",
      booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
      pages = "7735--7747",
      year = "2025"
  }

• Categorial Grammar Induction with Stochastic Category Selection.
  Christian Clark and William Schuler.
  Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), 2024.
  [paper] [code] [bibtex]
  [copy]

  @inproceedings{clarkschuler2024categorial,
      author = "Clark, Christian and Schuler, William",
      title = "Categorial Grammar Induction with Stochastic Category Selection",
      booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
      pages = "2893--2900",
      year = "2024"
  }

• Categorial Grammar Induction from Raw Data.
  Christian Clark and William Schuler.
  Findings of the Association for Computational Linguistics: ACL 2023, 2023.
  [paper] [code] [bibtex]
  [copy]

  @inproceedings{clarkschuler2023categorial,
      author = "Clark, Christian and Schuler, William",
      title = "Categorial Grammar Induction from Raw Data",
      booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
      pages = "2368--2379",
      year = "2023"
  }

• Comparison of Structural Parsers and Neural Language Models as Surprisal Estimators.
  Byung-Doh Oh, Christian Clark, and William Schuler.
  Frontiers in Artificial Intelligence, 2022.
  [paper] [code] [bibtex]
  [copy]

  @article{ohetal2022comparison,
      author = "Oh, Byung-Doh and Clark, Christian and Schuler, William",
      title = "Comparison of Structural Parsers and Neural Language Models as Surprisal Estimators",
      journal = "Frontiers in Artificial Intelligence",
      volume = "5",
      pages = "777963",
      year = "2022",
      publisher = "Frontiers Media SA"
  }

• Surprisal Estimators for Human Reading Times Need Character Models.
  Byung-Doh Oh, Christian Clark, and William Schuler.
  Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021.
  [paper] [code] [bibtex]
  [copy]

  @inproceedings{ohetal2021surprisal,
      author = "Oh, Byung-Doh and Clark, Christian and Schuler, William",
      title = "Surprisal Estimators for Human Reading Times Need Character Models",
      booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
      pages = "3746--3757",
      year = "2021"
  }

Presentations

• Word-Level Estimation of Shannon and Rényi Entropy Improves Reading Time Predictions.
  Christian Clark, Byung-Doh Oh, and William Schuler.
  39th Annual Conference on Human Sentence Processing, 2026.
  [abstract]
• What Transformer Attention Mechanism Provides the Best Fit for Human Reading Times?
  Lanni Bu, Xiulin Yang, Christian Clark, Alex Warstadt, and Ethan Wilcox.
  39th Annual Conference on Human Sentence Processing, 2026.
  [abstract]
• Improved Reading Time Predictions from Word-Level Contextual Entropy.
  Christian Clark, Byung-Doh Oh, and William Schuler.
  Computational Psycholinguistics Meeting, 2025.
  [abstract]
• Linear Recency Bias During Training Improves Transformers’ Fit to Reading Times.
  Christian Clark, Byung-Doh Oh, and William Schuler.
  Midwest Speech and Language Days, 2025.
• Effects of Recency Bias on Transformers’ Predictions of Reading Times.
  Christian Clark, Byung-Doh Oh, and William Schuler.
  38th Annual Conference on Human Sentence Processing, 2025.
  [abstract]
• Categorial Grammar Induction from Raw Data.
  Christian Clark and William Schuler.
  Midwest Speech and Language Days, 2024.
• Evidence for Composition Operations in Broad-Coverage Sentence Processing.
  Christian Clark and William Schuler.
  35th Annual Conference on Human Sentence Processing, 2022.
  [abstract]
• LaTeX for Linguists.
  Christian Clark, Daniel Puthawala, Adam Roth Singerman, and Jackson Lee.
  Ohio State LCC Tutorial, 2020–2025.
  [tutorial]

Invited Talks

• Understanding the Gaps Between Working Memory in Humans and Transformers.
  Invited talk (virtual) at Laboratoire de Sciences Cognitives et Psycholinguistique, École Normale Supérieure – PSL, March 2026.
• Toward Humanlike Memory Limitations in Transformer Language Models.
  Invited talk at Computation and Psycholinguistics Lab, New York University, October 2025.
• Effects of Recency Bias on Transformers' Predictions of Reading Times.
  Invited talk at University of Nottingham, May 2025.

Teaching

• Graduate Teaching Associate (Instructor of Record).
  LING 3802: Language and Computers.
  The Ohio State University, 2022–2023.
• Undergraduate Teaching Assistant.
  CS 0008: Introduction to Python Programming.
  University of Pittsburgh, 2015–2016.

Awards

• Dean's Distinguished University Fellowship.
  The Ohio State University, 2020–2022, 2025–2026.
• Outstanding Undergraduate Student.
  University of Pittsburgh Computer Science Department, 2017.
• Chancellor's Scholarship.
  University of Pittsburgh, 2013–2017.

Personal

I worked as an Applied Scientist for Amazon Alexa before graduate school. I enjoy playing the piano and organ in my free time.