Dan Jurafsky

Multilingual self-supervised speech representations improve the speech recognition of low-resource African languages with codeswitching

Tolúlopé Ògúnrèmí, Christopher D. Manning, Dan Jurafsky

Comments 5 pages, 1 figure. Computational Approaches to Linguistic Code-Switching, CALCS 2023 (co-located with EMNLP 2023)

Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models

Kaitlyn Zhou, Dan Jurafsky, Tatsunori Hashimoto

Comments EMNLP 2023 (Oral)

Injecting structural hints: Using language models to study inductive biases in language learning

Isabel Papadimitriou, Dan Jurafsky

Comments Findings of EMNLP 2023

Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models

Peter Henderson, Eric Mitchell, Christopher D. Manning, Dan Jurafsky, Chelsea Finn

Comments v1 Presented at the First Workshop of Pre-training: Perspectives, Pitfalls, and Paths Forward (ICML, 2022) and New Frontiers in Adversarial Machine Learning Workshop (ICML, 2022); v2 Presented at the Sixth AAAI/ACM Conference on AI, Ethics, and Society (AIES, 2023)

Developing Speech Processing Pipelines for Police Accountability

Anjalie Field, Prateek Verma, Nay San, Jennifer L. Eberhardt, Dan Jurafsky

Comments Accepted to INTERSPEECH 2023

Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale

Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, Aylin Caliskan

Comments FAccT 2023 paper. The published version is available at 10.1145/3593013.3594095

Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models

Myra Cheng, Esin Durmus, Dan Jurafsky

Comments To appear at ACL 2023, 9 pages, 3 figures, 3 tables

Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference

William Held, Dan Iter, Dan Jurafsky

Comments 9 pages, 8 figures, To be published in the 2021 Main Conference on Empirical Methods in Natural Language Processing

Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation

Martijn Bartelds, Nay San, Bradley McDonnell, Dan Jurafsky, Martijn Wieling

Comments Accepted at ACL 2023

string2string: A Modern Python Library for String-to-String Algorithms

Mirac Suzgun, Stuart M. Shieber, Dan Jurafsky

Comments GitHub: https://github.com/stanfordnlp/string2string; Documentation: http://string2string.readthedocs.io/

Multilingual BERT has an accent: Evaluating English influences on fluency in multilingual models

Isabel Papadimitriou, Kezia Lopez, Dan Jurafsky

Comments Findings of EACL 2023

Foundation Models and Fair Use

Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley, Percy Liang

When and why vision-language models behave like bags-of-words, and what to do about it?

Mert Yuksekgonul, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, James Zou

Comments ICLR 2023 Oral (notable-top-5%)

Leveraging supplementary text data to kick-start automatic speech recognition system development with limited transcriptions

Nay San, Martijn Bartelds, Blaine Billings, Ella de Falco, Hendi Feriza, Johan Safri, Wawan Sahrozi, Ben Foley, Bradley McDonnell, Dan Jurafsky

Comments Accepted for ComputEL-6

Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset

Peter Henderson, Mark S. Krass, Lucia Zheng, Neel Guha, Christopher D. Manning, Dan Jurafsky, Daniel E. Ho

Comments Presented at NeurIPS Datasets & Benchmarks (2022)

Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, Joelle Pineau

Comments Published in JMLR: https://jmlr.org/papers/v21/20-312.html

Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?

Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy Liang

Comments Published at NeurIPS 2022, presented at EAAMO 2022

Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding

Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky

Comments https://github.com/suzgunmirac/crowd-sampling

The Authenticity Gap in Human Evaluation

Kawin Ethayarajh, Dan Jurafsky

Comments EMNLP 2022

The Diversity-Innovation Paradox in Science

Bas Hofstra, Vivek V. Kulkarni, Sebastian Munoz-Najar Galvez, Bryan He, Dan Jurafsky, Daniel A. McFarland

Comments Updated paper; tightened up terminology, added better theoretical explanation, tested for a mechanism in the updated paper, added robustness analyses, updated and improved metrics across the board

Computationally Identifying Funneling and Focusing Questions in Classroom Discourse

Sterling Alic, Dorottya Demszky, Zid Mancenido, Jing Liu, Heather Hill, Dan Jurafsky

Comments 10 pages, 5 figures, North American Chapter of the Association for Computational Linguistics (NAACL) Workshop, Innovative Use of NLP for Building Educational Applications

On the Opportunities and Risks of Foundation Models

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, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, Aditi Raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

Comments Authored by the Center for Research on Foundation Models (CRFM) at the Stanford Institute for Human-Centered Artificial Intelligence (HAI). Report page with citation guidelines: https://crfm.stanford.edu/report.html

Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Models

Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky

Comments GitHub page: https://github.com/suzgunmirac/prompt-and-rerank. Project page: https://lukemelas.github.io/prompt-and-rerank/

Richer Countries and Richer Representations

Kaitlyn Zhou, Kawin Ethayarajh, Dan Jurafsky

Comments Camera Ready for ACL 2022 (Findings)

Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words

Kaitlyn Zhou, Kawin Ethayarajh, Dallas Card, Dan Jurafsky

Comments Camera Ready for ACL 2022 (Main Conference)

Modular Domain Adaptation

Junshen K. Chen, Dallas Card, Dan Jurafsky

Comments Findings of ACL (2022)

Automated speech tools for helping communities process restricted-access corpora for language revival efforts

Nay San, Martijn Bartelds, Tolúlopé Ògúnrèmí, Alison Mount, Ruben Thompson, Michael Higgins, Roy Barker, Jane Simpson, Dan Jurafsky

Comments Accepted at ComputEL-5

The Emergence of the Shape Bias Results from Communicative Efficiency

Eva Portelance, Michael C. Frank, Dan Jurafsky, Alessandro Sordoni, Romain Laroche

Comments Accepted at CoNLL 2021

Leveraging pre-trained representations to improve access to untranscribed speech from endangered languages

Nay San, Martijn Bartelds, Mitchell Browne, Lily Clifford, Fiona Gibson, John Mansfield, David Nash, Jane Simpson, Myfany Turpin, Maria Vollmer, Sasha Wilmoth, Dan Jurafsky

Comments Accepted at ASRU 2021

Nearest Neighbor Machine Translation

Urvashi Khandelwal, Angela Fan, Dan Jurafsky, Luke Zettlemoyer, Mike Lewis

Comments ICLR 2021