| 2026 | ACL | Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs. | Myra Cheng, Robert D. Hawkins, Dan Jurafsky |
| 2026 | ACL | Attention to Non-Adopters. | Kaitlyn Zhou, Kristina Gligoric, Myra Cheng, Michelle S. Lam, Vyoma Raman, Boluwatife Aminu, Caeley Woo, Michael Brockman, Hannah Cha, Dan Jurafsky |
| 2026 | CHI | Verbalizing LLMs' Assumptions About the User to Calibrate Expectations and Reduce Sycophancy. | Myra Cheng, Sunny Yu, Lujain Ibrahim, Diyi Yang, Dan Jurafsky |
| 2026 | EACL | Beyond Tokens: Concept-Level Training Objectives for LLMs. | Laya Iyer, Pranav Somani, Alice Guo, Dan Jurafsky, Chen Shani |
| 2026 | EACL | Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory. | Mirac Suzgun, Mert Yksekgnl, Federico Bianchi, Dan Jurafsky, James Zou |
| 2025 | ACL | HumT DumT: Measuring and controlling human-like language in LLMs. | Myra Cheng, Sunny Yu, Dan Jurafsky |
| 2025 | ASRU | Transcribe, Translate, or Transliterate: An Investigation of Intermediate Representations in Spoken Language Models. | Tollop gnrm, Christopher D. Manning, Dan Jurafsky, Karen Livescu |
| 2025 | CogSci | Soft production preferences emerge from a bottleneck on memory. | Neil Rathi, Richard Futrell, Dan Jurafsky |
| 2025 | EMNLP | False Friends Are Not Foes: Investigating Vocabulary Overlap in Multilingual Language Models. | Julie Kallini, Dan Jurafsky, Christopher Potts, Martijn Bartelds |
| 2025 | EMNLP | In-Context Learning Boosts Speech Recognition via Human-like Adaptation to Speakers and Language Varieties. | Nathan Roll, Calbert Graham, Yuka Tatsumi, Kim Tien Nguyen, Meghan Sumner, Dan Jurafsky |
| 2025 | ICASSP | Constructing Datasets From Public Police Body Camera Footage. | Jamie Rosas-Smith, Martijn Bartelds, Ruizhe Huang, Leibny Paola Garca-Perera, Karen Livescu, Dan Jurafsky, Anjalie Field |
| 2025 | ICLR | h4rm3l: A Language for Composable Jailbreak Attack Synthesis. | Moussa Koulako Bala Doumbouya, Ananjan Nandi, Gabriel Poesia, Davide Ghilardi, Anna Goldie, Federico Bianchi, Dan Jurafsky, Christopher D. Manning |
| 2025 | ICML | What can large language models do for sustainable food? | Anna T. Thomas, Adam Yee, Andrew Mayne, Maya B. Mathur, Dan Jurafsky, Kristina Gligoric |
| 2025 | ICML | AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders. | Zhengxuan Wu, Aryaman Arora, Atticus Geiger, Zheng Wang, Jing Huang, Dan Jurafsky, Christopher D. Manning, Christopher Potts |
| 2025 | Interspeech | The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties. | William Chen, Chutong Meng, Jiatong Shi, Martijn Bartelds, Shih-Heng Wang, Hsiu-Hsuan Wang, Rafael Mosquera, Sara Hincapie, Dan Jurafsky, Antonis Anastasopoulos, Hung-yi Lee, Karen Livescu, Shinji Watanabe |
| 2025 | NAACL | Can Unconfident LLM Annotations Be Used for Confident Conclusions? | Kristina Gligoric, Tijana Zrnic, Cinoo Lee, Emmanuel J. Cands, Dan Jurafsky |
| 2025 | NAACL | Rethinking Word Similarity: Semantic Similarity through Classification Confusion. | Kaitlyn Zhou, Haishan Gao, Sarah Li Chen, Dan Edelstein, Dan Jurafsky, Chen Shani |
| 2025 | NAACL | REL-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance. | Kaitlyn Zhou, Jena D. Hwang, Xiang Ren, Nouha Dziri, Dan Jurafsky, Maarten Sap |
| 2024 | ACL | CausalGym: Benchmarking causal interpretability methods on linguistic tasks. | Aryaman Arora, Dan Jurafsky, Christopher Potts |
| 2024 | ACL | string2string: A Modern Python Library for String-to-String Algorithms. | Mirac Suzgun, Stuart M. Shieber, Dan Jurafsky |
| 2024 | EACL | AnthroScore: A Computational Linguistic Measure of Anthropomorphism. | Myra Cheng, Kristina Gligoric, Tiziano Piccardi, Dan Jurafsky |
| 2024 | ICLR | Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions. | Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio, Paul Rttger, Dan Jurafsky, Tatsunori Hashimoto, James Zou |
| 2024 | ICLR | A Benchmark for Learning to Translate a New Language from One Grammar Book. | Garrett Tanzer, Mirac Suzgun, Eline Visser, Dan Jurafsky, Luke Melas-Kyriazi |
| 2024 | ICML | How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis. | Federico Bianchi, Patrick John Chia, Mert Yksekgnl, Jacopo Tagliabue, Dan Jurafsky, James Zou |
| 2024 | ICML | Model Alignment as Prospect Theoretic Optimization. | Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, Douwe Kiela |
| 2024 | Interspeech | A layer-wise analysis of Mandarin and English suprasegmentals in SSL speech models. | Antn de la Fuente, Dan Jurafsky |
| 2024 | Interspeech | ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets. | Jiatong Shi, Shih-Heng Wang, William Chen, Martijn Bartelds, Vanya Bannihatti Kumar, Jinchuan Tian, Xuankai Chang, Dan Jurafsky, Karen Livescu, Hung-yi Lee, Shinji Watanabe |
| 2024 | ICWSM | Othering and Low Status Framing of Immigrant Cuisines in US Restaurant Reviews and Large Language Models. | Yiwei Luo, Kristina Gligoric, Dan Jurafsky |
| 2024 | NAACL | NLP Systems That Can't Tell Use from Mention Censor Counterspeech, but Teaching the Distinction Helps. | Kristina Gligoric, Myra Cheng, Lucia Zheng, Esin Durmus, Dan Jurafsky |
| 2024 | NAACL | Grounding Gaps in Language Model Generations. | Omar Shaikh, Kristina Gligoric, Ashna Khetan, Matthias Gerstgrasser, Diyi Yang, Dan Jurafsky |
| 2023 | ACL | Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation. | Martijn Bartelds, Nay San, Bradley McDonnell, Dan Jurafsky, Martijn Wieling |
| 2023 | ACL | Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models. | Myra Cheng, Esin Durmus, Dan Jurafsky |
| 2023 | ACL | Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding. | Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky |
| 2023 | AIES | Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models. | Peter Henderson, Eric Mitchell, Christopher D. Manning, Dan Jurafsky, Chelsea Finn |
| 2023 | EACL | When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization. | Faisal Ladhak, Esin Durmus, Mirac Suzgun, Tianyi Zhang, Dan Jurafsky, Kathleen R. McKeown, Tatsunori Hashimoto |
| 2023 | EACL | Mini But Mighty: Efficient Multilingual Pretraining with Linguistically-Informed Data Selection. | Tollop gnrm, Dan Jurafsky, Christopher D. Manning |
| 2023 | EACL | Multilingual BERT has an accent: Evaluating English influences on fluency in multilingual models. | Isabel Papadimitriou, Kezia Lopez, Dan Jurafsky |
| 2023 | EMNLP | Injecting structural hints: Using language models to study inductive biases in language learning. | Isabel Papadimitriou, Dan Jurafsky |
| 2023 | EMNLP | Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models. | Kaitlyn Zhou, Dan Jurafsky, Tatsunori Hashimoto |
| 2023 | ICLR | When and Why Vision-Language Models Behave like Bags-Of-Words, and What to Do About It? | Mert Yksekgnl, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, James Zou |
| 2023 | Interspeech | Developing Speech Processing Pipelines for Police Accountability. | Anjalie Field, Prateek Verma, Nay San, Jennifer L. Eberhardt, Dan Jurafsky |
| 2022 | ACL | Modular Domain Adaptation. | Junshen K. Chen, Dallas Card, Dan Jurafsky |
| 2022 | ACL | Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words. | Kaitlyn Zhou, Kawin Ethayarajh, Dallas Card, Dan Jurafsky |
| 2022 | ACL | Richer Countries and Richer Representations. | Kaitlyn Zhou, Kawin Ethayarajh, Dan Jurafsky |
| 2022 | EMNLP | The Authenticity Gap in Human Evaluation. | Kawin Ethayarajh, Dan Jurafsky |
| 2022 | EMNLP | 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 |
| 2021 | ACL | Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions. | Dorottya Demszky, Jing Liu, Zid Mancenido, Julie Cohen, Heather Hill, Dan Jurafsky, Tatsunori Hashimoto |
| 2021 | ACL | Attention Flows are Shapley Value Explanations. | Kawin Ethayarajh, Dan Jurafsky |
| 2021 | ASRU | 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 |
| 2021 | CHI | SAD: A Stress Annotated Dataset for Recognizing Everyday Stressors in SMS-like Conversational Systems. | Matthew Louis Mauriello, Thierry Lincoln, Grace Hon, Dorien Simon, Dan Jurafsky, Pablo Paredes |
| 2021 | CoNLL | The Emergence of the Shape Bias Results from Communicative Efficiency. | Eva Portelance, Michael C. Frank, Dan Jurafsky, Alessandro Sordoni, Romain Laroche |
| 2021 | EMNLP | Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference. | William Barr Held, Dan Iter, Dan Jurafsky |
| 2021 | ICLR | Nearest Neighbor Machine Translation. | Urvashi Khandelwal, Angela Fan, Dan Jurafsky, Luke Zettlemoyer, Mike Lewis |
| 2021 | NAACL | Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation. | Yasuhide Miura, Yuhao Zhang, Emily Bao Tsai, Curtis P. Langlotz, Dan Jurafsky |
| 2021 | NAACL | Causal Effects of Linguistic Properties. | Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar |
| 2020 | AAAI | Automatically Neutralizing Subjective Bias in Text. | Reid Pryzant, Richard Diehl Martinez, Nathan Dass, Sadao Kurohashi, Dan Jurafsky, Diyi Yang |
| 2020 | ACL | Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models. | Dan Iter, Kelvin Guu, Larry Lansing, Dan Jurafsky |
| 2020 | ACL | Social Bias Frames: Reasoning about Social and Power Implications of Language. | Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, Yejin Choi |
| 2020 | EMNLP | With Little Power Comes Great Responsibility. | Dallas Card, Peter Henderson, Urvashi Khandelwal, Robin Jia, Kyle Mahowald, Dan Jurafsky |
| 2020 | EMNLP | Utility is in the Eye of the User: A Critique of NLP Leaderboards. | Kawin Ethayarajh, Dan Jurafsky |
| 2020 | EMNLP | DeSMOG: Detecting Stance in Media On Global Warming. | Yiwei Luo, Dallas Card, Dan Jurafsky |
| 2020 | EMNLP | Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models. | Isabel Papadimitriou, Dan Jurafsky |
| 2020 | ICLR | Generalization through Memorization: Nearest Neighbor Language Models. | Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, Mike Lewis |
| 2019 | CHI | Seekers, Providers, Welcomers, and Storytellers: Modeling Social Roles in Online Health Communities. | Diyi Yang, Robert E. Kraut, Tenbroeck Smith, Elijah Mayfield, Dan Jurafsky |
| 2019 | EMNLP | Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts. | Julia Kruk, Jonah Lubin, Karan Sikka, Xiao Lin, Dan Jurafsky, Ajay Divakaran |
| 2019 | NAACL | Recursive Routing Networks: Learning to Compose Modules for Language Understanding. | Ignacio Cases, Clemens Rosenbaum, Matthew Riemer, Atticus Geiger, Tim Klinger, Alex Tamkin, Olivia Li, Sandhini Agarwal, Joshua D. Greene, Dan Jurafsky, Christopher Potts, Lauri Karttunen |
| 2019 | NAACL | Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings. | Dorottya Demszky, Nikhil Garg, Rob Voigt, James Zou, Jesse Shapiro, Matthew Gentzkow, Dan Jurafsky |
| 2019 | NAACL | Let's Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms. | Diyi Yang, Jiaao Chen, Zichao Yang, Dan Jurafsky, Eduard H. Hovy |
| 2018 | ACL | Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context. | Urvashi Khandelwal, He He, Peng Qi, Dan Jurafsky |
| 2018 | CogSci | An Information-Theoretic Explanation of Adjective Ordering Preferences. | Michael Hahn, Judith Degen, Noah D. Goodman, Dan Jurafsky, Richard Futrell |
| 2018 | EMNLP | Framing and Agenda-Setting in Russian News: a Computational Analysis of Intricate Political Strategies. | Anjalie Field, Doron Kliger, Shuly Wintner, Jennifer Pan, Dan Jurafsky, Yulia Tsvetkov |
| 2018 | EMNLP | Textual Analogy Parsing: What's Shared and What's Compared among Analogous Facts. | Matthew Lamm, Arun Tejasvi Chaganty, Christopher D. Manning, Dan Jurafsky, Percy Liang |
| 2018 | LREC | JESC: Japanese-English Subtitle Corpus. | Reid Pryzant, Youngjoo Chung, Dan Jurafsky, Denny Britz |
| 2018 | LREC | RtGender: A Corpus for Studying Differential Responses to Gender. | Rob Voigt, David Jurgens, Vinodkumar Prabhakaran, Dan Jurafsky, Yulia Tsvetkov |
| 2018 | NAACL | Deconfounded Lexicon Induction for Interpretable Social Science. | Reid Pryzant, Kelly Shen, Dan Jurafsky, Stefan Wagner |
| 2018 | NAACL | Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction. | Ziang Xie, Guillaume Genthial, Stanley Xie, Andrew Y. Ng, Dan Jurafsky |
| 2018 | WWW | Community Interaction and Conflict on the Web. | Srijan Kumar, William L. Hamilton, Jure Leskovec, Dan Jurafsky |
| 2017 | ACL | Incorporating Dialectal Variability for Socially Equitable Language Identification. | David Jurgens, Yulia Tsvetkov, Dan Jurafsky |
| 2017 | EACL | A Two-stage Sieve Approach for Quote Attribution. | Grace Muzny, Michael Fang, Angel X. Chang, Dan Jurafsky |
| 2017 | EMNLP | Neural Net Models of Open-domain Discourse Coherence. | Jiwei Li, Dan Jurafsky |
| 2017 | EMNLP | Adversarial Learning for Neural Dialogue Generation. | Jiwei Li, Will Monroe, Tianlin Shi, Sbastien Jean, Alan Ritter, Dan Jurafsky |
| 2017 | ICLR | Data Noising as Smoothing in Neural Network Language Models. | Ziang Xie, Sida I. Wang, Jiwei Li, Daniel Lvy, Aiming Nie, Dan Jurafsky, Andrew Y. Ng |
| 2017 | ICWSM | Loyalty in Online Communities. | William L. Hamilton, Justine Zhang, Cristian Danescu-Niculescu-Mizil, Dan Jurafsky, Jure Leskovec |
| 2017 | ICWSM | Community Identity and User Engagement in a Multi-Community Landscape. | Justine Zhang, William L. Hamilton, Cristian Danescu-Niculescu-Mizil, Dan Jurafsky, Jure Leskovec |
| 2017 | SIGIR | Predicting Sales from the Language of Product Descriptions. | Reid Pryzant, Youngjoo Chung, Dan Jurafsky |
| 2016 | ACL | Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change. | William L. Hamilton, Jure Leskovec, Dan Jurafsky |
| 2016 | ACL | Predicting the Rise and Fall of Scientific Topics from Trends in their Rhetorical Framing. | Vinodkumar Prabhakaran, William L. Hamilton, Daniel A. McFarland, Dan Jurafsky |
| 2016 | EMNLP | Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora. | William L. Hamilton, Kevin Clark, Jure Leskovec, Dan Jurafsky |
| 2016 | EMNLP | Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change. | William L. Hamilton, Jure Leskovec, Dan Jurafsky |
| 2016 | EMNLP | Distinguishing Past, On-going, and Future Events: The EventStatus Corpus. | Ruihong Huang, Ignacio Cases, Dan Jurafsky, Cleo Condoravdi, Ellen Riloff |
| 2016 | EMNLP | Deep Reinforcement Learning for Dialogue Generation. | Jiwei Li, Will Monroe, Alan Ritter, Dan Jurafsky, Michel Galley, Jianfeng Gao |
| 2016 | Interspeech | Ketchup, Interdisciplinarity, and the Spread of Innovation in Speech and Language Processing. | Dan Jurafsky |
| 2016 | Interspeech | Between- and Within-Speaker Effects of Bilingualism on F0 Variation. | Rob Voigt, Dan Jurafsky, Meghan Sumner |
| 2016 | NAACL | Visualizing and Understanding Neural Models in NLP. | Jiwei Li, Xinlei Chen, Eduard H. Hovy, Dan Jurafsky |
| 2015 | ACL | A Hierarchical Neural Autoencoder for Paragraphs and Documents. | Jiwei Li, Minh-Thang Luong, Dan Jurafsky |
| 2015 | ACL | The Users Who Say 'Ni': Audience Identification in Chinese-language Restaurant Reviews. | Rob Voigt, Dan Jurafsky |
| 2015 | EMNLP | Do Multi-Sense Embeddings Improve Natural Language Understanding? | Jiwei Li, Dan Jurafsky |
| 2015 | EMNLP | When Are Tree Structures Necessary for Deep Learning of Representations? | Jiwei Li, Thang Luong, Dan Jurafsky, Eduard H. Hovy |
| 2015 | NAACL | Lexicon-Free Conversational Speech Recognition with Neural Networks. | Andrew L. Maas, Ziang Xie, Dan Jurafsky, Andrew Y. Ng |
| 2014 | CHI | Easy does it: more usable CAPTCHAs. | Elie Bursztein, Angelique Moscicki, Celine Fabry, Steven Bethard, John C. Mitchell, Dan Jurafsky |
| 2014 | CogSci | Learning to Reason Pragmatically with Cognitive Limitations. | Adam Vogel, Andrs Gomz Emilsson, Michael C. Frank, Dan Jurafsky, Christopher Potts |
| 2014 | ICWSM | How to Ask for a Favor: A Case Study on the Success of Altruistic Requests. | Tim Althoff, Cristian Danescu-Niculescu-Mizil, Dan Jurafsky |
| 2014 | LREC | On the Importance of Text Analysis for Stock Price Prediction. | Heeyoung Lee, Mihai Surdeanu, Bill MacCartney, Dan Jurafsky |
| 2013 | ACL | A computational approach to politeness with application to social factors. | Cristian Danescu-Niculescu-Mizil, Moritz Sudhof, Dan Jurafsky, Jure Leskovec, Christopher Potts |
| 2013 | ACL | Linguistic Models for Analyzing and Detecting Biased Language. | Marta Recasens, Cristian Danescu-Niculescu-Mizil, Dan Jurafsky |
| 2013 | ACL | Generating Recommendation Dialogs by Extracting Information from User Reviews. | Kevin Reschke, Adam Vogel, Dan Jurafsky |
| 2013 | ACL | Implicatures and Nested Beliefs in Approximate Decentralized-POMDPs. | Adam Vogel, Christopher Potts, Dan Jurafsky |
| 2013 | WWW | No country for old members: user lifecycle and linguistic change in online communities. | Cristian Danescu-Niculescu-Mizil, Robert West, Dan Jurafsky, Jure Leskovec, Christopher Potts |
| 2012 | ACL | Towards a Computational History of the ACL: 1980-2008. | Ashton Anderson, Dan Jurafsky, Daniel A. McFarland |
| 2012 | ACL | He Said, She Said: Gender in the ACL Anthology. | Adam Vogel, Dan Jurafsky |
| 2012 | EMNLP | Joint Entity and Event Coreference Resolution across Documents. | Heeyoung Lee, Marta Recasens, Angel X. Chang, Mihai Surdeanu, Dan Jurafsky |
| 2012 | ICDM | Learning Attitudes and Attributes from Multi-aspect Reviews. | Julian J. McAuley, Jure Leskovec, Dan Jurafsky |
| 2012 | ICML | Learning the Central Events and Participants in Unlabeled Text. | Nathanael Chambers, Dan Jurafsky |
| 2011 | ACL | Template-Based Information Extraction without the Templates. | Nathanael Chambers, Dan Jurafsky |
| 2011 | CoNLL | Stanford's Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task. | Heeyoung Lee, Yves Peirsman, Angel X. Chang, Nathanael Chambers, Mihai Surdeanu, Dan Jurafsky |
| 2011 | UIST | Sex, food, and words: the hidden meanings behind everyday language. | Dan Jurafsky |
| 2010 | CIKM | Who should I cite: learning literature search models from citation behavior. | Steven Bethard, Dan Jurafsky |
| 2010 | EMNLP | A Multi-Pass Sieve for Coreference Resolution. | Karthik Raghunathan, Heeyoung Lee, Sudarshan Rangarajan, Nate Chambers, Mihai Surdeanu, Dan Jurafsky, Christopher D. Manning |
| 2009 | ACL | Unsupervised Learning of Narrative Schemas and their Participants. | Nathanael Chambers, Dan Jurafsky |
| 2007 | ASRU | Automatic detection of contrastive elements in spontaneous speech. | Ani Nenkova, Dan Jurafsky |
| 2007 | ASRU | Regularization, adaptation, and non-independent features improve hidden conditional random fields for phone classification. | Yun-Hsuan Sung, Constantinos Boulis, Christopher D. Manning, Dan Jurafsky |
| 2007 | Interspeech | Modelling prominence and emphasis improves unit-selection synthesis. | Volker Strom, Ani Nenkova, Robert A. J. Clark, Yolanda Vazquez-Alvarez, Jason M. Brenier, Simon King, Dan Jurafsky |
| 2007 | SIGdial | Resolving "You" in Multi-Party Dialog. | Surabhi Gupta, John Niekrasz, Matthew Purver, Dan Jurafsky |
| 2001 | ICASSP | What kind of pronunciation variation is hard for triphones to model? | Dan Jurafsky, Wayne H. Ward, Zhang Banping, Keith Herold, Xiuyang Yu, Zhang Sen |