Christopher Potts +> Papers

  1. Nefdt, Ryan M. and Christopher Potts. 2024. Compositionality. In Michael C. Frank and Asifa Majid, eds., Open Encyclopedia of Cognitive Science. MIT Press. [bibtex]
  2. Kallini, Julie; Shikhar Murty; Christopher D. Manning; Christopher Potts; and Róbert Csordás. 2024. MrT5: Dynamic token merging for efficient byte-level language models. arXiv:2410.20771. [code; bibtex]
  3. Arora, Aryaman; Dan Jurafsky; Christopher Potts; Noah D. Goodman. 2024. Bayesian scaling laws for in-context learning. arXiv:2410.16531. [code; bibtex]
  4. Thrush, Tristan; Christopher Potts; and Tatsunori Hashimoto. 2024. Improving pretraining data using perplexity correlations. arXiv:2409.05816. [bibtex]
  5. D'Oosterlinck, Karel; Winnie Xu; Chris Develder; Thomas Demeester; Amanpreet Singh; Christopher Potts; Douwe Kiela; and Shikib Mehri. 2024. Anchored Preference Optimization and Contrastive Revisions: Addressing underspecification in alignment. arXiv:2408.06266. [code and data; bibtex]
  6. Potts, Christopher. 2024. Characterizing English Preposing in PP constructions. Journal of Linguistics. Published online 2024:1-39. [data and code; bibtex]
  7. Everaert, Dante; Rohit Patki; Tianqi Zheng; and Christopher Potts. 2024. AmazonQAC: A large-scale, naturalistic query autocomplete dataset. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, 1046–1055. Miami, Florida: Association for Computational Linguistics. [dataset; bibtex]
  8. Guo, Xuan; Rohit Patki; Dante Everaert; and Christopher Potts. 2024. Retrieval augmented spelling correction for E-commerce applications. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, 73–79. Miami, Florida: Association for Computational Linguistics. [bibtex]
  9. Soylu, Dilara; Christopher Potts; and Omar Khattab. 2024. Fine-tuning and prompt optimization: Two great steps that work better together. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 10696–10710. Miami, Florida: Association for Computational Linguistics. [DSPy; bibtex]
  10. Huang, Jing; Diyi Yang;* and Christopher Potts.* 2024. Demystifying verbatim memorization in large language models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 10711–10732. Miami, Florida: Association for Computational Linguistics. [code and data; bibtex]
  11. Opsahl-Ong, Krista; Michael J. Ryan; Josh Purtell; David Broman; Christopher Potts; Matei Zaharia; and Omar Khattab. 2024. Optimizing instructions and demonstrations for multi-stage language model programs. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 9340–9366. Miami, Florida: Association for Computational Linguistics. [DSPy; bibtex]
  12. Zur, Amir; Elisa Kreiss; Karel D'Oosterlinck; Christopher Potts; and Atticus Geiger. 2024. Updating CLIP to prefer descriptions over captions. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 20178–20187. Miami, Florida: Association for Computational Linguistics. [bibtex]
  13. Naik, Nandita Shankar; Christopher Potts; and Elisa Kreiss. 2024. CommVQA: Situating visual question answering in communicative contexts. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 13362–13377. Miami, Florida: Association for Computational Linguistics. [data; bibtex]
  14. Csordás, Róbert; Christopher Potts; Christopher D. Manning; and Atticus Geiger. 2024. Recurrent neural networks learn to store and generate sequences using non-linear representations. In Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, 248–262. Miami, Florida: Association for Computational Linguistics. [code and data; bibtex]
  15. Liu, Houjun; John Bauer; Karel D’Oosterlinck; Christopher Potts; and Christopher D. Manning. 2024. MSCAW-coref: Multilingual, singleton and conjunction-aware word-level coreference resolution. In Proceedings of The Seventh Workshop on Computational Models of Reference, Anaphora and Coreference, 33–40, Miami, Florida: Association for Computational Linguistics. [bibtex]
  16. Verma, Arnav; Kushin Mukherjee; Christopher Potts; Elisa Kreiss; and Judith E. Fan. 2024. Evaluating human and machine understanding of data visualizations. Proceedings of the Annual Meeting of the Cognitive Science Society, 46. [bibtex]
  17. Xian, Jasper; Saron Samuel, Faraz Khoubsirat, Ronak Pradeep, Md Arafat Sultan, Radu Florian, Salim Roukos, Avirup Sil, Christopher Potts, Omar Khattab. 2024. Prompts as auto-optimized training hyperparameters: Training best-in-class IR models from scratch with 10 gold labels. arXiv:2406.11706. [code; bibtex]
  18. Csordás, Róbert; Kazuki Irie; Jürgen Schmidhuber; Christopher Potts; Christopher D. Manning. 2024. MoEUT: Mixture-of-Experts Universal Transformers. To appear in Proceedings of NeurIPS. [code; bibtex]
  19. Wu, Zhengxuan;* Aryaman Arora;* Zheng Wang; Atticus Geiger; Dan Jurafsky; Christopher D. Manning; and Christopher Potts. 2024. ReFT: Representation finetuning for language models. To appear in Proceedings of NeurIPS. [pyreft; bibtex]
  20. Liang, Weixin;* Yaohui Zhang;* Zhengxuan Wu;* Haley Lepp; Wenlong Ji; Xuandong Zhao; Hancheng Cao; Sheng Liu; Siyu He; Zhi Huang; Diyi Yang; Christopher Potts; Christopher D. Manning; and James Y. Zou. 2024. Mapping the increasing use of LLMs in scientific papers. First Conference on Language Modeling. [bibtex]
  21. Wu, Zhengxuan; Atticus Geiger; Aryaman Arora; Jing Huang; Zheng Wang; Noah D. Goodman; Christopher D. Manning; and Christopher Potts. 2024. pyvene: A library for understanding and improving PyTorch models via interventions. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations), 158-165. Mexico City: Association for Computational Linguistics. [pyvene; bibtex]
  22. Saad-Falcon, Jon; Omar Khattab; Christopher Potts; and Matei Zaharia. 2024. ARES: An automated evaluation framework for retrieval-augmented generation systems. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 338-354. Mexico City: Association for Computational Linguistics. [data and code; bibtex]
  23. Huang, Jing; Zhengxuan Wu; Christopher Potts; Mor Geva; and Atticus Geiger. 2024. RAVEL: Evaluating interpretability methods on disentangling language model representations. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 8669–8687. Bangkok, Thailand: Association for Computational Linguistics. [code and data; bibtex]
  24. 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. Bangkok, Thailand: Association for Computational Linguistics. [Outstanding Paper Award; data; bibtex]
  25. 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. Bangkok, Thailand: Association for Computational Linguistics. [Best Paper Award; code; bibtex]
  26. Thrush, Tristan; Jared Moore; Miguel Monares; Christopher Potts; and Douwe Kiela. 2024. I am a Strange Dataset: Metalinguistic tests for language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 8888–8907. Bangkok, Thailand: Association for Computational Linguistics. [code and data; bibtex]
  27. D'Oosterlinck, Karel; Omar Khattab; François Remy; Thomas Demeester; Chris Develder; and Christopher Potts. 2024. In-context learning for extreme multi-label classification. arXiv:2401.12178. [code; bibtex]
  28. Wu, Zhengxuan; Atticus Geiger; Jing Huang; Aryaman Arora; Thomas Icard; Christopher Potts; and Noah D. Goodman. 2024. A reply to Makelov et al. (2023)'s “Interpretability Illusion” arguments. arXiv:2401.12631. [bibtex]
  29. Zaharia, Matei; Omar Khattab; Lingjiao Chen; Jared Quincy Davis; Heather Miller; Christopher Potts; James Zou; Michael Carbin; Jonathan Frankle; Naveen Rao; and Ali Ghodsi. 2024. The shift from models to Compound AI Systems. Berkeley AI Research Blog Post. [bibtex]
  30. Zhong, Peter Yong; Haoze He; Omar Khattab; Christopher Potts; Matei Zaharia; and Heather Miller. 2024. A guide to Large Language Model abstractions. Two Sigma Blog Post. [bibtex]
  31. Budur, Emrah; Rıza Özçelik; Dilara Soylu; Omar Khattab; Tunga Güngör; and Christopher Potts. 2024. Building efficient and effective OpenQA systems for low-resource languages. Knowledge-Based Systems 203: 112243. [code and data; bibtex]
  32. Khattab, Omar; Arnav Singhvi; Paridhi Maheshwari; Zhiyuan Zhang; Keshav Santhanam; Sri Vardhamanan; Saiful Haq; Ashutosh Sharma; Thomas T. Joshi; Hanna Moazam; Heather Miller; Matei Zaharia; and Christopher Potts. 2024. DSPy: Compiling declarative language model calls into self-improving pipelines. In Proceedings of The Twelfth International Conference on Learning Representations. [DSPy; bibtex]
  33. Everaert, Dante and Christopher Potts. 2024. GIO: Gradient Information Optimization for training dataset selection. In Proceedings of The Twelfth International Conference on Learning Representations. [code; bibtex]
  34. Kreiss, Elisa; Eric Zelikman; Christopher Potts; and Nick Haber. 2024. ContextRef: Evaluating referenceless metrics for image description generation. In Proceedings of The Twelfth International Conference on Learning Representations. [data and code; bibtex]
  1. Singhvi, Arnav; Manish Shetty; Shangyin Tan; Christopher Potts; Koushik Sen; Matei Zaharia; and Omar Khattab. 2023. DSPy Assertions: Computational constraints for self-refining language model pipelines. arXiv:2312.13382. [DSPy; bibtex]
  2. D'Oosterlinck, Karel; Thomas Demeester; Chris Develder; and Christopher Potts. 2023. Flexible model interpretability through natural language model editing. In BlackBoxNLP 2023. Singapore: Association for Computational Linguistics. [bibtex]
  3. D'Oosterlinck, Karel; Semere Kiros Bitew; Brandon Papineau; Christopher Potts; Thomas Demeester; Chris Develder. 2023. CAW-coref: Conjunction-Aware Word-level coreference resolution. In Proceeedings of the Sixth Workshop on Computational Models of Reference, Anaphora and Coreference. [code; bibtex]
  4. Zhang, Jingfen; Xuan Guo; Sravan Bodapati; and Christopher Potts. 2023. Multi-teacher distillation for multilingual spelling correction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, 142-151. Singapore: Association for Computational Linguistics. [bibtex]
  5. Huang, Jing; Atticus Geiger; Karel D'Oosterlinck; Zhengxuan Wu; and Christopher Potts. 2023. Rigorously assessing natural language explanations of neurons. In Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, 317–331. Singapore: Association for Computational Linguistics. [Best Paper Award; data; bibtex]
  6. Naik, Nandita; Christopher Potts; Elisa Kreiss. 2023. Context-VQA: Towards context-aware and purposeful Visual Question Answering. ICCV: 5th Workshop on Closing the Loop Between Vision and Language. [bibtex]
  7. She, Jingyuan Selena; Christopher Potts; Samuel R. Bowman; Atticus Geiger. 2023. ScoNe: Benchmarking negation reasoning in language models with fine-tuning and in-context learning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 1803-1821. Toronto: Association for Computational Linguistics. [data; bibtex]
  8. D'Oosterlinck, Karel; François Remy; Johannes Deleu; Thomas Demeester; Chris Develder; Klim Zaporojets; Aneiss Ghodsi; Simon Ellershaw; Jack Collins; and Christopher Potts. 2023. BioDEX: Large-scale biomedical adverse drug event extraction for real-world pharmacovigilance. In Findings of the Association for Computational Linguistics: EMNLP 2023, 13425-13454. Singapore: Association for Computational Linguistics. [data; bibtex]
  9. Zhong, Zexuan; Zhengxuan Wu; Christopher D. Manning; Christopher Potts; and Danqi Chen. 2023. MQuAKE: Assessing knowledge editing in language models via multi-hop questions. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 15686-15702. Singapore: Association for Computational Linguistics. [data; bibtex]
  10. Kreiss, Elisa; Krishna Srinivasan; Tiziano Piccardi; Jesus Adolfo Hermosillo; Cynthia Bennett; Michael S. Bernstein; Meredith Ringel Morris; and Christopher Potts. 2023. Characterizing image accessibility on Wikipedia across languages. Wiki Workshop 2023. [bibtex]
  11. Wu, Zhengxuan; Atticus Geiger; Christopher Potts; and Noah D. Goodman. 2023. Interpretability at scale: Identifying causal mechanisms in Alpaca. In Advances in Neural Information Processing Systems, 78205-78226. Curran Associates, Inc. [bibtex; data and code]
  12. Wu, Zhengxuan; Christopher D. Manning; and Christopher Potts. 2023. ReCOGS: How Incidental details of a logical form overshadow an evaluation of semantic interpretation. Transactions of the Association for Computational Linguistics 11: 1719-1733. [bibtex; data and code]
  13. Geiger, Atticus; Zhengxuan Wu; Christopher Potts; Thomas Icard; Noah D. Goodman. 2023. Finding alignments between interpretable causal variables and distributed neural representations. To appear in Proceedings of Causal Learning and Reasoning 2024. [bibtex]
  14. Saad-Falcon, Jon; Omar Khattab; Keshav Santhanam; Radu Florian; Martin Franz; Salim Roukos; Avirup Sil; Md Arafat Sultan; and Christopher Potts. 2023. UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 11265-11279. Singapore: Association for Computational Linguistics. [bibtex; code]
  15. Geiger, Atticus; Christopher Potts; and Thomas Icard. 2023. Causal abstraction for faithful model interpretation. Ms., Stanford University. [bibtex]
  16. Huang, Jing; Zhengxuan Wu; Kyle Mahowald; and Christopher Potts. 2023. Inducing character-level structure in subword-based language models with Type-level Interchange Intervention Training. In Findings of the Association for Computational Linguistics: ACL 2023, 12163-12180. Toronto: Association for Computational Linguistics. [bibtex]
  17. Sosa, Daniel N.; Malavika Suresh; Christopher Potts; and Russ B. Altman. 2023. Detecting contradictory COVID-19 drug efficacy claims from biomedical literature. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 1803-1821. Toronto: Association for Computational Linguistics. [bibtex; data and code]
  18. Santhanam, Keshav;* Jon Saad-Falcon;* Martin Franz; Omar Khattab; Avirup Sil; Radu Florian; Md Arafat Sultan; Salim Roukos; Matei Zaharia; and Christopher Potts. 2023. Moving beyond downstream task accuracy for information retrieval benchmarking. In Findings of the Association for Computational Linguistics: ACL 2023, 11613–11628. Toronto: Association for Computational Linguistics. [bibtex; code]
  19. Petersen, Erika and Christopher Potts. 2023. Lexical semantics with Large Language Models: A case study of English break. In Findings of the Association for Computational Linguistics: EACL 2023, 490–511. Dubrovnik: Association for Computational Linguistics. [bibtex; data and code]
  20. Wu, Zhengxuan;* Karel D'Oosterlinck;* Atticus Geiger;* Amir Zur; and Christopher Potts. 2023. Causal Proxy Models for concept-based model explanations. In Proceedings of ICML. [bibtex; data; code]
  1. Khattab, Omar; Keshav Santhanam; Xiang Lisa Li; David Hall; Percy Liang; Christopher Potts; and Matei Zaharia. 2022. Demonstrate–Search–Predict: Composing retrieval and language models for knowledge-intensive NLP. Ms., Stanford University. [bibtex; code]
  2. Geiger, Atticus; Zhengxuan Wu; Karel D'Oosterlinck; Elisa Kreiss; Noah D. Goodman; Thomas Icard; and Christopher Potts. 2022. Faithful, interpretable model explanations via causal abstraction. The Stanford AI Lab Blog. [bibtex]
  3. Li, Siyan; Riley Carlson; and Christopher Potts. 2022. Systematicity in GPT-3's interpretation of novel English noun compounds. In Findings of the Association for Computational Linguistics: EMNLP 2022, 717–728. [bibtex; data]
  4. Abraham, Eldar David;* Karel D'Oosterlinck;* Amir Feder;* Yair Ori Gat;* Atticus Geiger;* Christopher Potts;* Roi Reichart;* and Zhengxuan Wu.* 2022. CEBaB: Estimating the causal effects of real-world concepts on NLP model behavior. In Proceedings of NeurIPS, 17582-17596. [bibtex; data and code]
  5. Kreiss, Elisa; Cynthia Bennett; Shayan Hooshmand; Eric Zelikman; Meredith Ringel Morris; and Christopher Potts. 2022. Context matters for image descriptions for accessibility: Challenges for referenceless evaluation metrics. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 4685–4697. Association for Computational Linguistics. [bibtex; data and code]
  6. Kreiss, Elisa; Fei Fang; Noah D. Goodman; and Christopher Potts. 2022. Concadia: Towards image-based text generation with a purpose. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 4667–4684. Association for Computational Linguistics. [bibtex; data]
  7. Santhanam, Keshav;* Omar Khattab;* Christopher Potts; and Matei Zaharia. 2022. PLAID: An efficient engine for late interaction retrieval. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 1747–1756. Association for Computing Machinery. [bibtex; code]
  8. Fang, Fei; Kunal Sinha; Noah D. Goodman; Christopher Potts; and Elisa Kreiss. 2022. Color overmodification emerges from data-driven learning and pragmatic reasoning. In Proceedings of the Cognitive Science Society. [bibtex; code]
  9. Santhanam, Keshav;* Omar Khattab;* Jon Saad-Falcon; Christopher Potts; and Matei Zaharia. 2022. ColBERTv2: Effective and efficient retrieval via lightweight late interaction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 3715–3734. Association for Computational Linguistics. [bibtex]
  10. Wu, Zhengxuan;* Atticus Geiger;* Josh Rozner; Elisa Kreiss; Hanson Lu; Thomas Icard; Christopher Potts; and Noah D. Goodman. 2022. Causal distillation for language models. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4288-4295. Association for Computational Linguistics. [bibtex]
  11. Geiger, Atticus;* Zhengxuan Wu;* Hanson Lu;* Josh Rozner; Elisa Kreiss; Thomas Icard; Noah D. Goodman; and Christopher Potts. 2022. Inducing causal structure for interpretable neural networks. Proceedings of the 39th International Conference on Machine Learning, 7324-7338. PMLR. [bibtex]
  12. Geiger, Atticus; Alexandra Carstensen; Michael C. Frank; and Christopher Potts. 2022. Relational reasoning and generalization using non-symbolic neural networks. In Psychological Review. [bibtex; code]
  13. Wu, Zhengxuan; Nelson F. Liu; and Christopher Potts. 2022. Identifying the limits of cross-domain knowledge transfer for pretrained models. In Proceedings of the 7th Workshop on Representation Learning for NLP}, 100-110. Association for Computational Linguistics. [Best Paper Award; bibtex; code]
  14. Beam, Elizabeth; Christopher Potts; Russell A. Poldrack; and Amit Etkin. 2022. A data-driven framework for mapping domains of human neurobiology. Nature Neuroscience. [bibtex; Neuroscience Knowledge Engine]
  15. Paranjape, Ashwin; Omar Khattab; Christopher Potts; Matei Zaharia; and Christopher D. Manning. 2022. Hindsight: Posterior-guided training of retrievers for improved open-ended generation. In Proceedings of the International Conference on Learning Representations. [bibtex]
  1. Hooshmand, Shayan; Elisa Kreiss; and Christopher Potts. 2021. Intuitive image descriptions are context-sensitive. NeurIPS Workshop: Meaning in Context. [bibtex]
  2. Khattab, Omar; Christopher Potts; and Matei Zaharia. 2021. Building scalable, explainable, and adaptive NLP models with retrieval. The Stanford AI Lab Blog. [bibtex]
  3. Geiger, Atticus; Hanson Lu; Thomas Icard; and Christopher Potts. 2021. Causal abstractions of neural networks. In Proceedings of NeurIPS, 9574-9586. [bibtex]
  4. Khattab, Omar; Christopher Potts; and Matei Zaharia. 2021. Baleen: Robust multi-hop reasoning at scale via condensed retrieval. In Proceedings of NeurIPS, 27670-27682.[bibtex; code]
  5. Ma, Zhiyi; Kawin Ethayarajh; Tristan Thrush; Somya Jain; Ledell Wu; Robin Jia; Christopher Potts; Adina Williams; and Douwe Kiela. 2021. Dynaboard: An evaluation-as-a-service platform for holistic next-generation benchmarking. In Proceedings of NeurIPS, 10351-10367. [bibtex; platform]
  6. Rozner, Josh; Christopher Potts; and Kyle Mahowald. 2021. Decrypting cryptic crosswords: Semantically complex wordplay puzzles as a target for NLP. In Proceedings of NeurIPS, 11409-11421. [bibtex; data and code]
  7. Wu, Zhengxuan;* Elisa Kreiss;* Desmond C. Ong; and Christopher Potts. 2021. ReaSCAN: Compositional reasoning in language grounding. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks. [bibtex; data and code]
  8. West, Robert; Jure Leskovec; and Christopher Potts. 2021. Post-mortem memory of public figures in news and social media. Proceedings of the National Academy of Sciences 118(38): e2106152118. [bibtex; data and code]
  9. Khattab, Omar; Christopher Potts; and Matei Zaharia. 2021. Relevance-guided Supervision for OpenQA with ColBERT. Transactions of the ACL 9: 929-944. [bibtex]
  10. Adaptive Agents Group. 2021. The Shibboleth Rule for Artificial Agents. Stanford HAI Blog. [bibtex]
  11. Khattab, Omar; Christopher Potts; and Matei Zaharia. 2021. A moderate proposal for radically better AI-powered Web search. Stanford HAI blog. [bibtex]
  12. Adaptive Agents Group. 2021. When artificial agents lie, defame, and defraud, who is to blame? Stanford HAI Blog. [bibtex]
  13. Kiela, Douwe; Max Bartolo; Yixin Nie; Divyansh Kaushik; Atticus Geiger; Zhengxuan Wu; Bertie Vidgen; Grusha Prasad; Amanpreet Singh; Pratik Ringshia; Zhiyi Ma; Tristan Thrush; Sebastian Riedel; Zeerak Waseem; Pontus Stenetorp; Robin Jia; Mohit Bansal; Christopher Potts; and Adina Williams. 2021. Dynabench: Rethinking benchmarking in NLP. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4110-4124. Association for Computational Linguistics. [platform; bibtex]
  14. Potts, Christopher;* Zhengxuan Wu;* Atticus Geiger; and Douwe Kiela. 2021. DynaSent: A dynamic benchmark for sentiment analysis. In 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), 2388-2404. Association for Computational Linguistics. [bibtex; data and code]
  1. Todd, Graham; Shane Steinert-Threlkeld; and Christopher Potts. 2020. Learning compositional negation in populations of Roth-Erev and neural agents. Ms., Stanford University and the University of Washington. [bibtex]
  2. Potts, Christopher. 2020. Is it possible for language models to achieve language understanding? Medium post. [bibtex]
  3. Esposito, Lewis and Christopher Potts. 2020. A probabilistic pragmatics for English singular some. In Joseph Rhyne, Kaelyn Lamp, Nicole Dreier, and Chloe Kwon, eds., Proceedings of Semantics and Linguistic Theory 30, 22-42. Ithaca, NY: Cornell University. [bibtex; code]
  4. Kreiss, Elisa; Zijian Wang; and Christopher Potts. 2020. Modeling subjective assessments of guilt in newspaper crime narratives. In Proceedings of the 24th Conference on Computational Natural Language Learning (CoNLL), 56–68. Association for Computational Linguistics. [bibtex; data]
  5. Budur, Emrah; Rıza Özçelik; Tunga Güngör; and Christopher Potts. 2020. Data and Representation for Turkish Natural Language Inference. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 8253-8267. Association for Computational Linguistics. [bibtex; data]
  6. Geiger, Atticus; Kyle Richardson; and Christopher Potts. 2020. Neural natural language inference models partially embed theories of lexical entailment and negation. In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP 2020), 163-173. Association for Computational Linguistics. [bibtex; data]
  7. Nie, Allen; Reuben Cohn-Gordon; and Christopher Potts. 2020. Pragmatic Issue-Sensitive Image Captioning. In Findings of the Association for Computational Linguistics: EMNLP 2020, 1924-1938. [bibtex; code]
  8. Newman, Benjamin; Reuben Cohn-Gordon; and Christopher Potts. 2020. Communication-based evaluation for natural language generation. In Proceedings of the Society for Computation in Linguistics, 234-244. New Orleans: Linguistic Society of America. [bibtex]
  1. Geiger, Atticus; Ignacio Cases; Lauri Karttunen; and Christopher Potts. 2019. Posing fair generalization tasks for Natural Language Inference. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Hong Kong: Association for Computational Linguistics. [bibtex; data]
  2. Wang, Zijian and Christopher Potts. 2019. TalkDown: A corpus for condescension detection in context. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 3702-3710. Hong Kong: Association for Computational Linguistics. [bibtex; code and data]
  3. Bruno Godefroy and Christopher Potts. 2019. Modeling drug–disease relations with linguistic and knowledge graph constraints. Ms, Roam Analytics and Stanford University. [bibtex; data]
  4. Cases, Ignacio; Clemens Rosenbaum; Matthew Riemer; Atticus Geiger; Tim Klinger; Alex Tamkin; Olivia Li; Sandhini Agarwal; Joshua D. Greene; Dan Jurafsky; Christopher Potts; and Lauri Karttunen. 2019. Recursive routing networks: Learning to compose modules for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 3631-3648. Minneapolis: Association for Computational Linguistics. [bibtex]
  5. Tao, Yifeng; Bruno Godefroy; Guillaume Genthial; and Christopher Potts. 2019. Effective feature representation for clinical text concept extraction. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, 1-14. Minneapolis: Association for Computational Linguistics. [bibtex; code; data]
  6. Potts, Christopher. 2019. A case for deep learning in semantics: Response to Pater. Language 95(1): e115-e125. [bibtex]
  7. Cohn-Gordon, Reuben; Noah D. Goodman; and Christopher Potts. 2019. An incremental iterated response model of pragmatics. In Proceedings of the Society for Computation in Linguistics, 81-90. New York: Linguistic Society of America. Also presented at CoMPPrag2018: Computational Models of Language Generation and Processing in Pragmatics. [extended abstract; bibtex]
  1. Geiger, Atticus; Ignacio Cases; Lauri Karttunen; and Christopher Potts. 2018. Stress-testing neural models of natural language inference with multiply-quantified sentences. Ms., Stanford University. [bibtex; data]
  2. Kolchinski, Y. Alex and Christopher Potts. 2018. Representing social media users for sarcasm detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 1115-1121. Brussels, Belgium: Association for Computational Linguistics. [bibtex; code]
  3. Lengerich, Benjamin J.; Andrew L. Maas; and Christopher Potts. 2018. Retrofitting distributional embeddings to knowledge graphs with functional relations. Proceedings of the 27th International Conference on Computational Linguistics, 2423-2436. Santa Fe: Association for Computational Linguistics. [bibtex; code]
  4. Cohn-Gordon, Reuben; Noah D. Goodman; and Christopher Potts. 2018. Pragmatically informative image captioning with character-level inference. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 439-443. New Orleans: Association for Computational Linguistics. [bibtex; code]
  5. Dingwall, Nicholas and Christopher Potts. 2018. Mittens: An extension of GloVe for learning domain-specialized representations. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 212-217. New Orleans: Association for Computational Linguistics. [bibtex; mittens package; code]
  6. Monroe, Will; Jennifer Hu; Andrew Jong; and Christopher Potts. 2018. Generating bilingual pragmatic color references. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2155-2165. New Orleans: Association for Computational Linguistics. [bibtex; code and data]
  7. Srivastava, Sameer B.; Amir Goldberg; V. Govind Manian; and Christopher Potts. 2018. Enculturation trajectories: language, cultural adaptation, and individual outcomes in organizations. Management Science 64(3):1348-1364. [Best Paper Award, 2016 Wharton People Analytics Conference; bibtex]
  1. Monroe, Will; Robert X.D. Hawkins; Noah D. Goodman; and Christopher Potts. 2017. Colors in context: a pragmatic neural model for grounded language understanding. Transactions of the Association for Computational Linguistics 5: 325-338. [bibtex; code and data]
  2. Cases, Ignacio; Minh-Thang Luong; and Christopher Potts. 2017. On the effective use of pretraining for natural language inference. Ms., Stanford University. [bibtex]
  3. de Marneffe, Marie-Catherine and Christopher Potts. 2017. Developing linguistic theories using annotated corpora. In Nancy Ide and James Pustejovsky, eds., The Handbook of Linguistic Annotation, 411-438. Berlin: Springer. [This article was written in 2014; bibtex]
  1. Frank, Michael C.; Andrés Gómez Emilsson; Benjamin Peloquin; Noah D. Goodman; and Christopher Potts. 2016. Rational speech act models of pragmatic reasoning in reference games. Ms., Stanford University. [bibtex; code and data]
  2. Goldberg, Amir; Sameer B. Srivastava; V. Govind Manian; Will Monroe; and Christopher Potts. 2016. Fitting in or standing out? The tradeoffs of structural and cultural embeddedness. American Sociological Review 81(6): 1190-1222. [Best Paper Awards, 2015 Wharton People Analytics Conference and 2015 Kellogg Computational Social Science Summit; bibtex]
  3. Monroe, Will; Noah D. Goodman; and Christopher Potts. 2016. Learning to generate compositional color descriptions. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2243-2248. Austin, TX: Association for Computational Linguistics. [bibtex; code and data]
  4. Potts, Christopher; Daniel Lassiter; Roger Levy; and Michael C. Frank. 2016. Embedded implicatures as pragmatic inferences under compositional lexical uncertainty. Journal of Semantics 33(4): 755-802. [bibtex; slides; code and data]
  5. Jeong, Sunwoo and Christopher Potts. 2016. Intonational sentence-type conventions for perlocutionary effects: An experimental investigation. In Mary Moroney, Carol-Rose Little, Dan Burgdorf, and Jacob Collard, eds., Proceedings of Semantics and Linguistic Theory 26, 1-22. Ithaca, NY: CLC Publications. [bibtex; code and data]
  6. Bowman, Samuel R.; Jon Gauthier; Abhinav Rastogi; Raghav Gupta; Christopher D. Manning; and Christopher Potts. 2016. A fast unified model for parsing and sentence understanding. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 1466-1477. Berlin: Association for Computational Linguistics. [bibtex; code]
  1. Bowman, Samuel R.; Christopher D. Manning; and Christopher Potts; 2015. Tree-structured composition in neural networks without tree-structured architectures. In Proceedings of the NIPS 2015 Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches. Montreal. [bibtex]
  2. Monroe, Will and Christopher Potts. 2015. Learning in the Rational Speech Acts model. In Proceedings of the 20th Amsterdam Colloquium. Amsterdam: ILLC. [bibtex]
  3. Bowman, Samuel R.; Gabor Angeli; Christopher Potts; and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 632-642. Lisboa, Portugal: Association for Computational Linguistics. [Best New Data Set or Resource Award; bibtex; Stanford Natural Language Inference Corpus]
  4. Bowman, Samuel R.; Christopher Potts; and Christopher D. Manning. 2015. Recursive neural networks can learn logical semantics. In Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality. Beijing: Association for Computational Linguistics. [bibtex; code and data]
  5. Chang, Angel; Will Monroe; Manolis Savva; Christopher Potts; and Christopher D. Manning. 2015. Text to 3d scene generation with rich lexical grounding. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference of the Asian Federation of Natural Language Processing, 53-62. Beijing: Association for Computational Linguistics. [bibtex; data]
  6. Potts, Christopher and Roger Levy. 2015. Negotiating lexical uncertainty and speaker expertise with disjunction. In Anna E. Jurgensen, Hannah Sande, Spencer Lamoureux, Kenny Baclawski, and Alison Zerbe, eds., Proceedings of the 41st Annual Meeting of the Berkeley Linguistics Society, 417-445. Berkeley Linguistics Society. [bibtex; associated LSA poster; code and data]
  7. de Marneffe, Marie-Catherine; Marta Recasens; and Christopher Potts. 2015. Modeling the lifespan of discourse entities with application to coreference resolution. Journal of Artificial Intelligence Research 52: 445-475. [bibtex]
  8. Bowman, Samuel R.; Christopher Potts; and Christopher D. Manning. 2015. Learning distributed word representations for natural logic reasoning. In Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches: Papers from the 2015 AAAI Spring Symposium, 10-13. AAAI Publications. [bibtex; code and data]
  9. Liang, Percy and Christopher Potts. 2015. Bringing machine learning and compositional semantics together. Annual Review of Linguistics 1(1): 355-376. [bibtex; reference implementations]
  10. Potts, Christopher. 2015. Presupposition and implicature. In Shalom Lappin and Chris Fox, eds., The Handbook of Contemporary Semantic Theory, 2nd edn, 168-202. Oxford: Wiley-Blackwell. [bibtex]
  1. West, Robert; Hristo S. Paskov; Jure Leskovec; and Christopher Potts. 2014. Exploiting social network structure for person-to-person sentiment analysis. Transactions of the Association for Computational Linguistics 2(2): 297-310. [bibtex; data and supplementary materials]
  2. Sudhof, Moritz; Andrés Gómez Emilsson; Andrew L. Maas; and Christopher Potts. 2014. Sentiment expression conditioned by affective transitions and social forces. In Proceedings of 20th Conference on Knowledge Discovery and Data Mining, 1136-1145. New York. [poster; bibtex]
  3. Vogel, Adam; Andrés Gómez Emilsson; Michael C. Frank; Dan Jurafsky; and Christopher Potts. 2014. Learning to reason pragmatically with cognitive limitations. In Proceedings of the 36th Annual Meeting of the Cognitive Science Society, 3055-3060. Quebec City: Cognitive Science Society. [bibtex; code and data]
  4. Acton, Eric K. and Christopher Potts. 2014. That straight talk: Sarah Palin and the sociolinguistics of demonstratives. Journal of Sociolinguistics 18(1): 3-31. [bibtex; code and data]
  1. Potts, Christopher. 2013. Conversational implicature: interacting with grammar. Ms., Stanford University. [bibtex; overview; code; counterexamples to Hurford's constraint]
  2. 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. [bibtex; supplementary materials; data, code, and annotation tools]
  3. Vogel, Adam; Christopher Potts; and Dan Jurafsky. 2013. Implicatures and nested beliefs in approximate Decentralized-POMDPs. In Proceedings of the 2013 Annual Conference of the Association for Computational Linguistics, 74-80. Sofia, Bulgaria: Association for Computational Linguistics. [bibtex; Cards corpus]
  4. Danescu-Niculescu-Mizil, Cristian; Moritz Sudhof; Dan Jurafsky; Jure Leskovec; and Christopher Potts. 2013. A computational approach to politeness with application to social factors. In Proceedings of the 2013 Annual Conference of the Association for Computational Linguistics, 250-259. Sofia, Bulgaria: Association for Computational Linguistics. [Best Paper Award nomination; bibtex; data and info]
  5. Vogel, Adam; Max Bodoia; Christopher Potts; and Dan Jurafsky. 2013. Emergence of Gricean maxims from multi-agent decision theory. In Human Language Technologies: The 2013 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 1072-1081. Atlanta, GA: Association for Computational Linguistics. [bibtex; Cards corpus]
  6. Recasens, Marta; Marie-Catherine de Marneffe; and Christopher Potts. 2013. The life and death of discourse entities: identifying singleton mentions. In Human Language Technologies: The 2013 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 627-633. Atlanta, GA: Association for Computational Linguistics. [Best Short Paper Award; bibtex; Stanford Deterministic Coreference Resolution System]
  7. Danescu-Niculescu-Mizil, Cristian; Robert West; Dan Jurafsky; Jure Leskovec; and Christopher Potts. 2013. No country for old members: user lifecycle and linguistic change in online communities. Proceedings of the 22nd World Wide Web Conference, 307-317. Rio de Janeiro: ACM. [Best Paper Award; bibtex; data and info]
  1. Potts, Christopher. 2012. Goal-driven answers in the Cards dialogue corpus. In Nathan Arnett and Ryan Bennett, eds., Proceedings of the 30th West Coast Conference on Formal Linguistics, 1-20. Somerville, MA: Cascadilla Press. [bibtex; Cards corpus and the paper's code]
  2. de Marneffe, Marie-Catherine; Christopher D. Manning; and Christopher Potts. 2012. Did it happen? The pragmatic complexity of veridicality assessment. Computational Linguistics 38(2): 301-333. [bibtex; PragBank]
  3. Overgoor, Jan; Ellery Wulczyn; and Christopher Potts. 2012. Trust propagation with mixed-effects models. Proceedings of the 6th International AAAI Conference on Weblogs and Social Media. Dublin, Ireland: Association for the Advancement of Artificial Intelligence. [bibtex]
  4. Djalali, Alex; Sven Lauer; and Christopher Potts. 2012. Corpus evidence for preference-driven interpretation. In Maria Aloni, Vadim Kimmelman, Floris Roelofsen, Galit Weidman Sassoon, Katrin Schulz, and Matthijs Westera, eds., Proceedings of the 18th Amsterdam Colloquium: Revised Selected Papers, 150-159. Berlin: Springer. [bibtex; Cards corpus]
  5. Potts, Christopher. 2012. Conventional implicature and expressive content. In Claudia Maienborn, Klaus von Heusinger, and Paul Portner, eds., Semantics: An International Handbook of Natural Language Meaning, Volume 3, 2516-2536 Berlin: Mouton de Gruyter. [This article was written in 2008; bibtex]
  1. Maas, Andrew L.; Andrew Y. Ng; and Christopher Potts. 2011. Multi-dimensional sentiment analysis with learned representations. Technical report, Stanford Computer Science and Stanford Linguistics, April 2011. [bibtex; supplementary diagram]
  2. Djalali, Alex; David Clausen; Sven Lauer; Karl Schultz; and Christopher Potts. 2011. Modeling expert effects and common ground using Questions Under Discussion. Proceedings of the AAAI Workshop on Building Representations of Common Ground with Intelligent Agents. Washington, DC: Association for the Advancement of Artificial Intelligence. [bibtex; Cards corpus]
  3. de Marneffe, Marie-Catherine, Christopher D. Manning and Christopher Potts. 2011. Veridicality and utterance meaning. Proceedings of the Fifth IEEE International Conference on Semantic Computing: Workshop on Semantic Annotation for Computational Linguistic Resources, Stanford, CA: IEEE Computer Society Press. [bibtex; PragBank]
  4. 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. [bibtex; data]
  5. Potts, Christopher. To appear. Pragmatics. In Ruslan Mitkov, ed., The Oxford Handbook of Computational Linguistics, 2nd edition. Oxford University Press. [bibtex]
  6. Miller, Mahalia, Conal Sathi, Daniel Wiesenthal, Jure Leskovec, and Christopher Potts. 2011. Sentiment flow through hyperlink networks. Proceedings of the 5th International AAAI Conference on Weblogs and Social Media. Barcelona, Spain: Association for the Advancement of Artificial Intelligence. [bibtex]
  7. Potts, Christopher. 2011. Teaching pragmatics. In Koenraad Kuiper, ed., Teaching Linguistics, 51-65. London: Equinox Publishing. [bibtex]
  8. Potts, Christopher. 2011. On the negativity of negation. In Nan Li and David Lutz, eds., Proceedings of Semantics and Linguistic Theory 20, 636-659. Ithaca, NY: CLC Publications. [bibtex; data viewer; code and data]
  1. de Marneffe, Marie-Catherine, Christopher D. Manning and Christopher Potts. 2010. "Was it good? It was provocative." Learning the meaning of scalar adjectives. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 167-176. Uppsala, Sweden: Association for Computational Linguistics. [bibtex; code and data]
  2. Munro, Robert, Steven Bethard, Victor Kuperman, Vicky Tzuyin Lai, Robin Melnick, Christopher Potts, Tyler Schnoebelen, and Harry Tily. 2010. Crowdsourcing and language studies: The new generation of linguistic data. Proceedings of the NAACL 2010 Workshop on Creating Speech and Language Data With Amazon’s Mechanical Turk, 122-130. Los Angeles, CA: Association for Computational Linguistics. [bibtex]
  3. Potts, Christopher and Florian Schwarz. 2010. Affective 'this'. Linguistic Issues in Language Technology 3(5):1-30. [bibtex; code and data]
  4. Potts, Christopher, Joe Pater, Karen Jesney, Rajesh Bhatt, and Michael Becker. 2010. Harmonic Grammar with Linear Programming: From linear systems to linguistic typology. Phonology 27(1):1-41. [bibtex; OT-Help]
  5. Davis, Christopher and Christopher Potts. 2010. Affective demonstratives and the division of pragmatic labor. In Maria Aloni, Harald Bastiaanse, Tikitu de Jager, and Katrin Schulz, eds., Logic, Language, and Meaning: 17th Amsterdam Colloquium Revised Selected Papers, 42-52. Berlin: Springer. [bibtex; UMass Amherst Sentiment Corpora]
  6. Potts, Christopher. 2010. Semantics–pragmatics interactions. In Patrick Colm Hogan, ed., The Cambridge Encyclopedia of the Language Sciences, 758-759. Cambridge University Press. [bibtex]
  1. Harris, Jesse A. and Christopher Potts. 2009. Perspective-shifting with appositives and expressives. Linguistics and Philosophy 32(6):523-552. [bibtex; embedded appositives data]
  2. de Marneffe, Marie, Scott Grimm, and Christopher Potts. 2009. Not a simple yes or no: Uncertainty in indirect answers. Proceedings of the 10th Annual SIGDIAL Meeting on Discourse and Dialogue, 136-143. Queen Mary University of London: Association for Computational Linguistics. [bibtex]
  3. Harris, Jesse A. and Christopher Potts. 2009. Predicting perspectival orientation for appositives. In Ryan Bochnak, Nassira Nicola, Peet Klecha, Jasmin Urban, Alice Lemieux, and Christina Weaver, eds., Proceedings of the 45th Annual Chicago Linguistic Society Meeting: The Main Session, 207-221. Chicago Linguistic Society. [bibtex]
  4. Constant, Noah; Christopher Davis; Christopher Potts; and Florian Schwarz. 2009. The pragmatics of expressive content: Evidence from large corpora. Sprache und Datenverarbeitung 33(1-2):5-21. [bibtex; UMass Amherst Sentiment Corpora]
  5. Potts, Christopher, Luis Alonso-Ovalle, Ash Asudeh, Rajesh Bhatt, Seth Cable, Christopher Davis, Yurie Hara, Angelika Kratzer, Eric McCready, Tom Roeper, and Martin Walkow. 2009. Expressives and identity conditions. Linguistic Inquiry 40(2):356-366. [bibtex]
  6. Potts, Christopher. 2009. Formal pragmatics. In Louise Cummings, ed., The Routledge Encyclopedia of Pragmatics, 167-170. London: Routledge. [bibtex]
  1. Potts, Christopher. 2008. Review article: Hagit Borer's Structuring Sense, Volumes I and II. Language 84(2):343-369. [bibtex]
  2. Potts, Christopher. 2008. Indirect answers and cooperation: On Asher and Lascarides's 'Making the right commitments in dialogue'. Commentary paper for the University of Michigan Linguistics and Philosophy Workshop on Implicatures, Nov 21-23. [bibtex]
  3. Potts, Christopher and Florian Schwarz. 2008. Exclamatives and heightened emotion: Extracting pragmatic generalizations from large corpora. Ms., UMass Amherst. [bibtex; UMass Amherst Sentiment Corpora]
  4. Potts, Christopher. 2008. Interpretive Economy, Schelling Points, and evolutionary stability. Ms., UMass Amherst. [bibtex]
  5. Potts, Christopher. 2008. Wait a minute! What kind of discourse strategy is this? Ms., UMass Amherst. [bibtex; annotated data set]
  1. Pater, Joe, Rajesh Bhatt, and Christopher Potts. 2007. Linguistic optimization. Ms., UMass Amherst. [bibtex]
  2. Davis, Christopher, Christopher Potts, and Margaret Speas. 2007. The pragmatic values of evidential sentences. In Masayuki Gibson and Tova Friedman, eds., Proceedings of Semantics and Linguistic Theory 17, 71-88. Ithaca, NY: CLC Publications. [bibtex]
  3. Potts, Christopher. 2007. The expressive dimension. Theoretical Linguistics 33(2):165-197. [bibtex]
  4. Potts, Christopher. 2007. The centrality of expressive indices: Reply to the commentaries. Theoretical Linguistics 33(2):255-268. [bibtex]
  5. Potts, Christopher. 2007. Into the conventional-implicature dimension. Philosophy Compass 4(2):665-679. [bibtex]
  6. Potts, Christopher. 2007. The dimensions of quotation. In Chris Barker and Pauline Jacobson, eds., Direct Compositionality, 405-431. Oxford University Press. [bibtex]
  7. Potts, Christopher. 2007. Conventional implicatures, a distinguished class of meanings. In Gillian Ramchand and Charles Reiss, eds., The Oxford Handbook of Linguistic Interfaces, 475-501. Oxford University Press. [bibtex]
  1. Potts, Christopher. 2006. How far can pragmatic mechanisms take us? Theoretical Linguistics 32(3):307-320. [bibtex]
  2. Potts, Christopher and Tom Roeper. 2006. The narrowing acquisition path: From expressive small clauses to declaratives. In Ljiljana Progovac, Kate Paesani, Eugenia Casielles, Ellen Barton, eds., The Syntax of Nonsententials: Multi-Disciplinary Perspectives, 183-201. John Benjamins. [bibtex]
  3. Potts, Christopher. 2006. Review of Siobhan Chapman, Paul Grice: Philosopher and Linguist. Mind 115(459):743-747. [bibtex]
  4. Potts, Christopher. 2006. Conversational implicatures via general pragmatic pressures. In Takashi Washio, Akito Sakurai, Katsuto Nakajima, Hideaki Takeda, Satoshi Tojo, and Makoto Yokoo, eds., Japanese Society for Artificial Intelligence 2006, 205-218. Berlin: Springer. [bibtex]
  1. Potts, Christopher. 2005. The Logic of Conventional Implicatures. Oxford University Press. [bibtex]
  2. Potts, Christopher. 2005. Lexicalized intonational meaning. In Shigeto Kawahara, ed., University of Massachusetts Occasional Papers 30 (UMOP 30), 129-146. Amherst, MA: GLSA. [bibtex]
  1. Potts, Christopher and Shigeto Kawahara. 2004. Japanese honorifics as emotive definite descriptions. In Kazuha Watanabe and Robert B. Young, eds., Proceedings of Semantics and Linguistic Theory 14, 235-254. Ithaca, NY: CLC Publications. [bibtex]
  1. Potts, Christopher. 2003. The Logic of Conventional Implicatures. PhD thesis, UC Santa Cruz. [abstract; bibtex]
  2. Potts, Christopher. 2003. Expressive content as conventional implicature. In Makoto Kadowaki and Shigeto Kawahara, eds., Proceedings of the North East Linguistic Society 33, 303-322. UMass Amherst: GLSA. [bibtex]
  1. Potts, Christopher and Geoffrey K. Pullum. 2002. Model theory and the content of OT constraints. Phonology 19(3):361-393. [bibtex]
  2. Potts, Christopher. 2002. The syntax and semantics of As-parentheticals. Natural Language and Linguistic Theory 20(3):623-689. [bibtex]
  3. Potts, Christopher. 2002. The lexical semantics of parenthetical-as and appositive-which. Syntax 5(1):55-88. [bibtex]
  4. Mikkelsen, Line and Christopher Potts. 2002. WCCFL 21: Proceedings of the 21st West Coast Conference on Formal Linguistics. Somerville, MA: Cascadilla Press. [bibtex]
  5. Potts, Christopher. 2002. NELS 33 conference report. Glot International.
  6. Potts, Christopher. 2002. WCCFL 21 conference report. Glot International 5:5.
  7. Potts, Christopher. 2002. No vacuous quantification constraints in syntax. In Masako Hirotani, ed., Proceedings of the North East Linguistic Society 32, 451-470. University of Massachusetts, Amherst: GLSA. [bibtex]
  8. Potts, Christopher. 2002. Comparative economy conditions in natural language syntax. Paper presented at the North American Summer School in Logic, Language, and Information, Workshop on Model-Theoretic Syntax, Stanford University, June 28. [bibtex]
  1. Potts, Christopher. 2001. (Only) some weak crossover effects repaired. Snippets 1:3. [bibtex]
  2. Potts, Christopher. 2001. Three kinds of transderivational constraint. In Séamas Mac Bhloscaidh, ed., Syntax at Santa Cruz, Volume 3, 21-40. Linguistics Department, UC Santa Cruz. [bibtex]
  1. Potts, Christopher. 2000. When even no's neg is splitsville. Jorge Hankamer's Web Fest. [pdf; bibtex]
  1. Potts, Christopher. 1999. Vehicle change in anti-pronominal contexts. Ms., NYU Linguistics. [bibtex]
  1. Potts, Christopher. 1988.
    Potts 1988: Word lists and very primitive dependency parses, by a 4th grade Chris Potts. At the top is a list of negitives [sic]: no, none, never, not.
    Potts 1988: Very primitive dependency parses drawn by a 4th grade Chris Potts, with numerous spelling errors.