Christopher Potts

Christopher Potts (commonly known as Chris Potts) is an American linguist and cognitive scientist. He is Professor and Chair of the Department of Linguistics and, by courtesy, Professor of Computer Science at Stanford University. He is best known for work in formal semantics and pragmatics, including an influential multidimensional approach to expressivity, conventional implicature, and their kin. His more recent work develops probabilistic models of pragmatic inference that bridge theoretical and experimental approaches.

Christopher Potts
Alma mater	New York University University of California, Santa Cruz
Known for	Formal semantics Pragmatic theory Probabilistic models of language
Awards	See Awards and honors section
Scientific career
Institutions	University of Massachusetts Amherst (2003–2009) Stanford University (2009–present)
Thesis	The Logic of Conventional Implicatures  (2003)
Doctoral advisor	Geoffrey K. Pullum
Website	Official Stanford profile

He gave plenary talks at the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing,^[1] the 2016 Conference on Empirical Methods in Natural Language Processing,^[2] and the 2025 convention of the Linguistics Society of America.^[3]

Academic career

Potts earned his Bachelor of Arts in Linguistics from New York University (1999) and his Ph.D. in Linguistics from the University of California, Santa Cruz (2003), with a dissertation titled The Logic of Conventional Implicatures advised by Geoffrey K. Pullum. After his doctoral studies, he joined the faculty at the University of Massachusetts Amherst (2003–2009). In 2009, he joined Stanford University, where he now serves as Chair of the Linguistics Department and holds a courtesy appointment in the Computer Science Department. His interdisciplinary work is widely cited in studies of language processing, formal semantics, and natural language understanding.^[4]

Research

Potts's research centers on formal semantics and pragmatic reasoning. He has developed probabilistic extensions of the Rational Speech Act model that elucidate how speakers and listeners resolve ambiguity and uncertainty. His work has advanced the understanding of quantification, modality, and the syntax–semantics interface, and it is influential across linguistics, cognitive science, and computational language research.

Selected publications

• Potts, C. (2003). The Logic of Conventional Implicatures. Ph.D. thesis, University of California, Santa Cruz.
• Maas, Andrew L.; Raymond E. Daly; Peter T. Pham; Dan Huang; Andrew Y. Ng; and Christopher Potts. (2011). Learning word vectors for sentiment analysis. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, Portland, OR: Association for Computational Linguistics.
• Socher, Richard; Alex Perelygin; Jean Wu; Jason Chuang; Christopher D. Manning, Andrew Y. Ng; and Christopher Potts. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 1631-1642. Seattle, WA: Association for Computational Linguistics.
• Arora, Aryaman; Dan Jurafsky; and Christopher Potts. (2024). CausalGym: Benchmarking causal interpretability methods on linguistic tasks. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 14638–14663.
• Kallini, Julie; Isabel Papadimitriou; Richard Futrell; Kyle Mahowald; and Christopher Potts. (2024). Mission: Impossible Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 14691-14714.

Awards and honors

Potts's contributions have been recognized with numerous awards:

• ACL Test of Time Award (2023)^[5]
• Dean's Award for Distinguished Teaching, Stanford University (2015–2016)^[6]
• Best Paper Award, ACL (2024)^[7]
• Outstanding Paper Award, ACL (2024)^[7]
• Best Paper Award, BlackboxNLP (2023)^[8]
• Best New Data Set or Resource Award, Conference on Empirical Methods in Natural Language Processing (2015)^[9]
• Best Paper Award, The 22nd World Wide Web Conference (2013)^[10]
• Best Short Paper Award, The Annual Conference of the North American Chapter of the Association for Computational Linguistics (2013)^[11]