William Schuler

William Schuler
PECASE awardee (more info, pic)

Arts and Sciences Distinguished Professor and Chair,
Department of Linguistics,
The Ohio State University

Computational Cognitive Modeling Lab
Center for Cognitive Sciences

Teaching:

Selected Publications:

Surprisal from Larger Transformer-based Language Models Predicts fMRI Data More Poorly.
Yi-Chien Lin and William Schuler.
Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL'26), Rabat, Morocco, 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 (IJCNLP/AACL'25), Mumbai, India, 2025.
The Impact of Token Granularity on the Predictive Power of Language Model Surprisal.
Byung-Doh Oh and William Schuler.
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL'25), Vienna, Austria, 2025.
Are Larger Language Models Better at Disambiguation?
Ziyuan Cao and William Schuler.
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL'25), Albuquerque, New Mexico, 2025.
The Inverse Scaling Effect of Pre-Trained Language Model Surprisal Is Not Due to Data Leakage.
Byung-Doh Oh, Hongao Zhu and William Schuler.
Findings of the Association for Computational Linguistics: ACL 2025, Vienna, Austria, 2025.
Linear Recency Bias During Training Improves Transformers' Fit to Reading Times.
Christian Clark, Byung-Doh Oh, William Schuler.
Proceedings of the 31st International Conference on Computational Linguistics (COLING'25), Abu Dhabi, UAE, 2025.
Dissociable frequency effects attenuate as large language model surprisal predictors improve.
Byung-Doh Oh and William Schuler.
Journal of Memory and Language, 143:e104645, Elsevier, 2025.
A deep learning approach to analyzing continuous-time cognitive processes.
Cory Shain and William Schuler.
Open Mind 8:235–264, 2024.
Leading Whitespaces of Language Models' Subword Vocabulary Pose a Confound for Calculating Word Probabilities.
Byung-Doh Oh and William Schuler.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP'24), Miami, Florida, 2024.
Evaluation of an Algorithmic-Level Left-Corner Parsing Account of Surprisal Effects.
William Schuler and Shisen Yue.
Cognitive Science 48(10):e13500, Wiley, 2024.
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'24), Torino, Italy, 2024.
Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times.
Byung-Doh Oh, Shisen Yue and William Schuler.
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL'24), Valletta, Malta, 2024.
Transformer-Based LM Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens.
Byung-Doh Oh and William Schuler.
Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore, 2023.
Categorial grammar induction from raw data.
Christian Clark and William Schuler.
Findings of the Association for Computational Linguistics: ACL 2023, 2368–2379, Toronto, Canada, 2023.
Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions.
Byung-Doh Oh 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 (ACL'23), 10105–10117, Toronto, Canada, 2023.
Bootstrapping a Conversational Guide for Colonoscopy Prep.
Pulkit Arya, Madeleine Bloomquist, Subhankar Chakraborty, Andrew Perrault, William Schuler, Eric Fosler-Lussier and Michael White.
24th Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL'23), 413–420, 2023.
Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?
Byung-Doh Oh and William Schuler.
Transactions of the Association for Computational Linguistics, 11: 336–350, 2023.
Robust Effects of Working Memory Demand in Language-Selective Cortex.
Cory A Shain, Idan Blank, Evelina Fedorenko, Edward Gibson and William Schuler.
Proceedings of the 36th Annual Conference on Human Sentence Processing (HSP'23), Pittsburgh, Pennsylvania, 2023.
On the Bigger-is-Worse Nature of Pre-Trained Language Model Surprisal.
Byung-Doh Oh and William Schuler.
Proceedings of the 36th Annual Conference on Human Sentence Processing (HSP'23), Pittsburgh, Pennsylvania, 2023.
Memory-Based Predictors From GPT-2 Attention Predict Reading Times Over Surprisal.
Byung-Doh Oh and William Schuler.
Proceedings of the 36th Annual Conference on Human Sentence Processing (HSP'23), Pittsburgh, Pennsylvania, 2023.
Artificial intelligence in virtual standardized patients: Combining natural language understanding and rule based dialogue management to improve conversational fidelity.
Kellen R. Maicher, Adam Stiff, Marisa Scholl, Michael White, Eric Fosler-Lussier, William Schuler, Prashant Serai, Vishal Sunder, Hannah Forrestal, Lexi Mendella, Mahsa Adib, Camille Bratton, Kevin Lee and Douglas R. Danforth.
Medical Teacher, 45(3):279–285, Taylor and Francis, 2022.
Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal.
Byung-Doh Oh and William Schuler.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP'22), Abu Dhabi, United Arab Emirates, 2022.
Robust Effects of Working Memory Demand during Naturalistic Language Comprehension in Language-Selective Cortex.
Cory Shain, Idan A. Blank, Evelina Fedorenko, Edward Gibson and William Schuler.
Journal of Neuroscience, 42(39):7412–7430, Soc Neuroscience, 2022.
Comparison of structural parsers and neural language models as surprisal estimators.
Byung-Doh Oh, Christian Clark and William Schuler.
Frontiers in Artificial Intelligence, 5:777963, Frontiers, Mar 2022.
Evidence for Composition Operations in Broad-Coverage Sentence Processing.
Christian Clark and William Schuler,
Proceedings of the 35th Annual Conference on Human Sentence Processing (HSP), Santa Cruz, California, 2022.
Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages.
Lifeng Jin, Byung-Doh Oh, William Schuler
Findings of the Association for Computational Linguistics: EMNLP 2021, Punta Cana, Dominican Republic (online), 2021.
Coreference-aware Surprisal Predicts Brain Response.
Evan Jaffe, Byung-Doh Oh, William Schuler,
Findings of the Association for Computational Linguistics: EMNLP 2021, Punta Cana, Dominican Republic (online), 2021.
Surprisal Estimators for Human Reading Times Need Character Models.
Byung-Doh Oh, Christian Clark, William Schuler,
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL'21), Bangkok, Thailand (online), 2021.
Contributions of Propositional Content and Syntactic Category Information in Sentence Processing.
Byung-Doh Oh, William Schuler,
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL'21), Mexico City, Mexico (online), 2021.
Continuous-time deconvolutional regression for psycholinguistic modeling.
Cory Shain, William Schuler,
Cognition, 215:104735, Oct 2021.
Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition.
Lifeng Jin, Lane Schwartz, Finale Doshi-Velez, Timothy Miller, William Schuler,
Computational Linguistics, 47(1):181–216, 2021.
Comparison of Structural and Neural Language Models as Surprisal Estimators.
Byung-Doh Oh, Christian Clark, William Schuler,
CUNY Sentence Processing Conference, Philadelphia, Pennsylvania (online), 2021.
Contributions of Propositional Content and Syntactic Categories in Sentence Processing.
Byung-Doh Oh, William Schuler,
CUNY Sentence Processing Conference, Philadelphia, Pennsylvania (online), 2021.
Coreference information guides human expectations during natural reading.
Evan Jaffe, Cory Shain, William Schuler,
Proceedings of the 28th International Conference on Computational Linguistics (COLING'20), Barcelona, Spain (online), 2020.
Grounded PCFG Induction with Images.
Lifeng Jin, William Schuler,
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing (AACL'20), Suzhou, China, 2020.
Memory-bounded Neural Incremental Parsing for Psycholinguistic Prediction.
Lifeng Jin, William Schuler,
Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies Online, July 2020.
The Importance of Category Labels in Grammar Induction with Child-directed Utterances.
Lifeng Jin, William Schuler,
Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies Online, July 2020.
A Corpus of Encyclopedia Articles with Logical Forms.
Nathan Rasmussen, William Schuler,
Proceedings of the 12th Language Resources and Evaluation Conference (LREC'20), Marseille, France, 2020.
fMRI reveals language-specific predictive coding during naturalistic sentence comprehension.
Cory Shain, Idan Asher Blank, Marten van Schijndel, William Schuler, Evelina Fedorenko,
Neuropsychologia, 138, Feb 2020.
Prediction in the language network is sensitive to syntactic structure.
Cory Shain, Idan Blank, Marten van Schijndel, Evelina Fedorenko, William Schuler,
CUNY Sentence Processing Conference, Amherst, Massachusetts, 2020.
Coreference information guides human expectations during naturalistic reading.
Evan Jaffe, Cory Shain, William Schuler,
CUNY Sentence Processing Conference, Amherst, Massachusetts, 2020.
Unsupervised Learning of PCFGs with Normalizing Flow.
Lifeng Jin, Finale Doshi-Velez, Timothy Miller, Lane Schwartz and William Schuler,
Proceedings of the 2019 meeting of the Association for Computational Linguistics (ACL'19), Florence, Italy, 2019.
Variance of average surprisal: a better predictor for quality of grammar from unsupervised PCFG induction.
Lifeng Jin and William Schuler,
Proceedings of the 2019 meeting of the Association for Computational Linguistics (ACL'19), Florence, Italy, 2019.
Using virtual standardized patients to accurately assess information gathering skills in medical students.
Kellen R Maicher, Laura Zimmerman, Bruce Wilcox, Beth Liston, Holly Cronau, Allison Macerollo, Lifeng Jin, Evan Jaffe, Michael White, Eric Fosler-Lussier, William Schuler, David P Way, Douglas R Danforth,
Medical Teacher, 41(9):1053–1059, 2019.
fMRI reveals language-specific predictive coding during naturalistic sentence comprehension.
Cory Shain, Idan Blank, Marten van Schijndel, William Schuler and Evelina Fedorenko,
CUNY Sentence Processing Conference, University of Colorado, Boulder, 2019.
Depth-bounding is Effective: Improvements and Evaluation of Unsupervised PCFG Induction.
Lifeng Jin, Finale Doshi-Velez, Timothy Miller, William Schuler and Lane Schwartz,
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP'18), Brussels, Belgium, 2018.
Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects.
Cory Shain and William Schuler,
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP'18), Brussels, Belgium, 2018.
Unsupervised Grammar Induction with Depth-bounded PCFG.
Lifeng Jin, Finale Doshi-Velez, Timothy Miller, William Schuler, Lane Schwartz,
Transactions of the Association for Computational Linguistics, 6:211–224, 2018.
Test Sets for Chinese Nonlocal Dependency Parsing.
Manjuan Duan and William Schuler,
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC'18), Miyazaki, Japan, 2018.
Deep syntactic annotations for broad-coverage psycholinguistic modeling.
Cory Shain, Marten van Schijndel and William Schuler,
Workshop on Linguistic and Neuro-Cognitive Resources, Miyazaki, Japan, 2018.
Coreference and Focus in Reading Times.
Evan Jaffe, Cory Shain and William Schuler,
Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL'18), pages 1--9, Salt Lake City, UT, 2018.
Modeling psycholinguistic effect timecourses with deconvolutional time series regression.
Cory Shain and William Schuler,
CUNY Sentence Processing Conference, University of California, Davis, 2018.
Coreference and discourse focus in broad-coverage stimuli.
Evan Jaffe, Cory Shain and William Schuler,
CUNY Sentence Processing Conference, University of California, Davis, 2018.
Evidence of semantic processing difficulty in naturalistic reading.
Cory Shain, Richard Futrell, Marten van Schijndel, Edward Gibson, William Schuler and Evelina Fedorenko,
CUNY Sentence Processing Conference, University of California, Davis, 2018.
Left-Corner Parsing with Distributed Associative Memory Produces Surprisal and Locality Effects.
Nathan Rasmussen, William Schuler,
Cognitive Science, 42(S4):1009–1042, Wiley, 2018.
Approximations of Predictive Entropy Correlate with Reading Times.
Marten van Schijndel, William Schuler,
Proceedings of the 39th Annual meeting of the Cognitive Science Society (CogSci'17), pages 1260--1265, London, UK, 2017.
What does coreference cost? Regressing reading times to coreference-based predictors.
Evan Jaffe, Cory Shain, William Schuler,
Midwest Cognitive Science Conference, Miami University, 2017.
Retrieving structures from memory causes difficulty during incremental processing.
Cory Shain, Marten van Schijndel, Richard Futrell, Edward Gibson and William Schuler,
CUNY Sentence Processing Conference, Massachusetts Institute of Technology, 2017.
Modeling syntax acquisition via cognitively-constrained unsupervised grammar induction.
Cory Shain, William Bryce, Lifeng Jin, Victoria Krakovna, Finale Doshi-Velez, Timothy Miller, William Schuler and Lane Schwartz,
CUNY Sentence Processing Conference, Massachusetts Institute of Technology, 2017.
Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input.
Cory Shain, William Bryce, Lifeng Jin, Victoria Krakovna, Finale Doshi-Velez, Timothy Miller, William Schuler and Lane Schwartz,
Proceedings of the 26th International Conference on Computational Linguistics (COLING'16), Osaka, Japan, 2016.
Addressing surprisal deficiencies in reading time models.
Marten van Schijndel and William Schuler,
Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC), Osaka, Japan, 2016.
Memory access during incremental sentence processing causes reading time latency.
Cory Shain, Marten van Schijndel, Richard Futrell, Edward Gibson and William Schuler,
Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC), Osaka, Japan, 2016.
Investigating Locality Effects and Surprisal in Written English Syntactic Choice Phenomena.
Rajakrishnan Rajkumar, Marten van Schijndel, Michael White, and William Schuler,
Cognition, 155:204–232, 2016.
OCLSP at SemEval-2016 Task 9: Multilayered LSTM as a Neural Semantic Dependency Parser.
Lifeng Jin, Manjuan Duan and William Schuler,
Proceedings of SemEval, San Diego, CA, 2016.
OSU CHGCG at SemEval-2016 Task 9: Chinese Semantic Dependency Parsing with Generalized Categorial Grammar.
Manjuan Duan, Lifeng Jin and William Schuler,
Proceedings of SemEval, San Diego, CA, 2016.
Alpha power decreases during center embedding in natural stimuli.
Marten van Schijndel and William Schuler,
CUNY Sentence Processing Conference, University of Florida, 2016.
Exploring memory and processing through a gold standard annotation of Dundee.
Cory Shain, Marten van Schijndel, Edward Gibson and William Schuler,
CUNY Sentence Processing Conference, University of Florida, 2016.
Parsing Chinese with a Generalized Categorial Grammar.
Manjuan Duan and William Schuler,
Proceedings of the Grammar Engineering Across Frameworks Workshop (GEAF'15), Beijing, China, 2015.
Evidence of syntactic working memory usage in MEG data.
Marten van Schijndel, Brian Murphy and William Schuler,
Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics (CMCL'15), Denver, CO, 2015.
Interpreting Questions with a Log-Linear Ranking Model in a Virtual Patient Dialogue System.
Evan Jaffe, Michael White, William Schuler, Eric Fosler-Lussier, Alex Rosenfeld, and Douglas Danforth,
Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications (BEA'15), Denver, CO, 2015.
Hierarchic syntax improves reading time prediction.
Marten van Schijndel and William Schuler,
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics -- Human Language Technologies (NAACL-HLT 2015), Denver, CO, 2015.
A Comparison of Word Similarity Performance Using Explanatory and Non-explanatory Texts.
Lifeng Jin and William Schuler,
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics -- Human Language Technologies (NAACL-HLT 2015), Denver, CO, 2015.
Hierarchic syntax improves reading time prediction.
Marten van Schijndel and William Schuler,
CUNY Sentence Processing Conference, Los Angeles, CA, 2015.
Cognitive Compositional Semantics using Continuation Dependencies (the slides have more background).
William Schuler and Adam Wheeler,
Third Joint Conference on Lexical and Computational Semantics (*SEM'14), Dublin, Ireland, 2014.
Sentence Processing in a Vectorial Model of Working Memory (the slides are somewhat friendlier).
William Schuler,
Proceedings of the 5th Workshop on Cognitive Modeling and Computational Linguistics (CMCL'14), Baltimore, MA, 2014.
Frequency Effects in the Processing of Unbounded Dependencies.
Marten van Schijndel, William Schuler and Peter Culicover,
In Proceedings of the 36th Annual Meeting of the Cognitive Science Society (CogSci 2014), Quebec City, Canada, 2014.
Effects of integration in eye tracking.
William Schuler and Marten van Schijndel,
CUNY Sentence Processing Conference, Columbus, OH, 2014.
An Analysis of Memory-based Processing Costs using Incremental Deep Syntactic Dependency Parsing.
Marten van Schijndel, Luan Nguyen and William Schuler,
Proceedings of the 4th Workshop on Cognitive Modeling and Computational Linguistics (CMCL'13), Sofia, Bulgaria, 2013.
A Model of Language Processing as Hierarchic Sequential Predictions.
Marten van Schijndel, Andrew Exley and William Schuler,
Topics in Cognitive Science, 5(4)522–540, Wiley, 2013.
An Analysis of Frequency- and Recency-Based Processing Costs.
Marten van Schijndel and William Schuler,
Proceedings of 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT'13), Atlanta, GA, 2013.
Accurate Unbounded Dependency Recovery using Generalized Categorial Grammars.
Luan Nguyen, Marten van Schijndel and William Schuler,
Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012), Mumbai, India, 2012.
(winner, Best Research Paper)
Connectionist-Inspired Incremental PCFG Parsing.
Marten van Schijndel, Andy Exley and William Schuler,
Proceedings of the 3rd Workshop on Cognitive Modeling and Computational Linguistics (CMCL'12), Montreal, Canada, 2012.
Effects of Filler-gap Dependencies on Working Memory Requirements for Parsing.
William Schuler,
Proceedings of the 33rd Annual meeting of the Cognitive Science Society (CogSci'11), Boston, MA, 2011.
Incremental Syntactic Language Models for Phrase-based Translation.
Lane Schwartz, Chris Callison-Burch, William Schuler and tephen Wu,
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT'11), Portland, OR, 2011.
A Pronoun Anaphora Resolution System based on Factorial Hidden Markov Models.
Dingcheng Li, Tim Miller and William Schuler,
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT'11), Portland, OR, 2011.
Structured Composition of Semantic Vectors.
Stephen Wu and William Schuler,
Proceedings of the International Conference on Computational Semantics, Oxford, UK, 2011.
Invited Tutorial: Incremental Parsing in Bounded Memory (proceedings paper) / (tutorial slides).
William Schuler,
Proceedings of the 10th International Conference on Tree Adjoining Grammars and Related Formalisms (TAG+10), New Haven, CT, 2010.
(an invited tutorial on HHMM parsing using a model-based right-corner transform).
Complexity Metrics in an Incremental Right-corner Parser.
Stephen Wu, Asaf Bachrach, Carlos Cardenas and William Schuler,
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL'10), Uppsala, Sweden, 2010.
HHMM Parsing with Limited Parallelism.
Tim Miller and William Schuler,
Proceedings of the Workshop on Cognitive Modeling in Computational Linguistics, Uppsala, Sweden, 2010.
Broad-coverage Parsing using Human-like Memory Constraints.
William Schuler, Samir AbdelRahman, Tim Miller and Lane Schwartz.
Computational Linguistics, 36(1):1–30, MIT Press, 2010.
A Framework for Fast Incremental Interpretation during Speech Decoding.
William Schuler, Stephen Wu and Lane Schwartz.
Computational Linguistics, 35(3):313–343, MIT Press, 2009.
Parsing Speech Repair without Specialized Grammar Symbols.
Tim Miller, Luan Nguyen and William Schuler,
Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics (ACL'09), Singapore, 2009.
Parsing with a Bounded Stack using a Model-based Right-corner Transform.
William Schuler,
Proceedings of the North American Association for Computational Linguistics (NAACL'09), Boulder, Colorado, 2009.
Positive Effects of Redundant Descriptions in an Interactive Semantic Speech Interface.
Lane Schwartz, Luan Nguyen, Andrew Exley and William Schuler,
Proceedings of the 2009 International Conference on Intelligent User Interfaces (IUI'09), Sanibel Island, FL, 2009.
A Syntactic Time-Series Model for Parsing Fluent and Disfluent Speech.
Tim Miller and William Schuler,
Proceedings of the 22nd International Conference on Computational Linguistics (COLING'08), Manchester, UK, 2008.
Toward a Psycholinguistically-Motivated Model of Language Processing.
William Schuler, Samir AbdelRahman, Tim Miller and Lane Schwartz,
Proceedings of the 22nd International Conference on Computational Linguistics (COLING'08), Manchester, UK, 2008.
A Unified Syntactic Model for Parsing Fluent and Disfluent Speech.
Tim Miller and William Schuler,
Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics (ACL'08), Columbus, OH, 2008.
Referential Semantic Language Modeling for Data-Poor Domains.
Stephen Wu, Lane Schwartz and William Schuler,
Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP'08), Las Vegas, Nevada, 2008.
Exploiting Referential Context in Spoken Language Interfaces for Data-Poor Domains.
Stephen Wu, Lane Schwartz and William Schuler,
Proceedings of the 2008 International Conference on Intelligent User Interfaces (IUI'08), Canary Islands, Spain, 2008.
Incremental Semantic Models for Continuous Context-Sensitive Speech Recognition.
Tim Miller, Lane Schwartz and William Schuler,
Proceedings of the Workshop on Semantic Representation of Spoken Language (SRSL'07), Salamanca, Spain, 2007.
Elements of a Spoken Language Programming Interface for Robots.
Tim Miller, Andy Exley and William Schuler,
Proceedings of the 2nd ACM/IEEE International Conference on Human Robot Interaction (HRI'07), Washington, D.C., 2007.
Dynamic Evidence Models in a DBN Phone Recognizer.
William Schuler, Tim Miller, Andrew Exley and Stephen Wu,
Proceedings of the 9th International Conference on Spoken Language Processing (ICSLP/Interspeech'06), pages 1221–1224, Pittsburgh, Pennsylvania, 2006.
Integrating Denotational Meaning into a DBN Language Model.
William Schuler and Tim Miller,
Proceedings of the 9th European Conference on Speech Communication and Technology (Eurospeech/Interspeech'05), Lisbon, Portugal, 2005.
Exploiting a Sensed Environment to Improve Human-Agent Communication
Shana Watters, Tim Miller, Praveen Balachandran, William Schuler and Richard Voyles,
Proceedings of the 4th International Joint Conference on Autonomous Agents and Multi-Agent Systems (AA/MAS'05), pages 44–50, Utrecht, Netherlands, 2005.
On relations of constituency and dependency grammars.
Mark Dras, David Chiang and William Schuler,
Research on Language and Computation 2(2):281–305, Kluwer, 2004.
Using model-theoretic semantic interpretation to guide statistical parsing and word recognition in a spoken language interface.
William Schuler,
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL'03), pages 529–536, Sapporo, Japan, 2003.
Interleaved semantic interpretation in environment-based parsing.
William Schuler,
Proceedings of the 19th International Conference on Computational Linguistics (COLING'02), pages 897–903, Taipei, Taiwan, 2002.
ACUMEN: Amplifying control and understanding of multiple entities.
Jan Allbeck, Karin Kipper, Charles Adams, William Schuler, Elena Zoubanova, Norm Badler, Martha Palmer and Aravind Joshi.
Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems (AA/MAS'02), pages 191–198, Bologna, Italy, 2002.
Computational properties of environment-based disambiguation.
William Schuler,
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics (ACL'01), pages 466–473, Toulouse, France, 2001.
Multi-component TAG and notions of formal power.
William Schuler, David Chiang and Mark Dras,
Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics (ACL'00), pages 448–455, Hong Kong, China, 2000.
Dynamically altering agent behaviors using natural language instructions.
Rama Bindiganavale, William Schuler, Jan Allbeck, Norman Badler, Aravind Joshi and Martha Palmer,
Proceedings of the 4th Conference on Autonomous Agents (AA'00), pages 191–198, Barcelona, Spain, 2000.
A machine translation system from English to American Sign Language.
Liwei Zhao, Karin Kipper, William Schuler and Norman Badler,
Proceedings of the 4th Conference of the Association for Machine Translation in the Americas (AMTA'00), pages 293–300, Cuernavaca, Mexico, 2000.
Preserving dependencies in synchronous tree adjoining grammars.
William Schuler,
Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL'99), pages 88–95, College Park, Maryland, 1999.
Restrictions on tree adjoining grammars.
Giorgio Satta and William Schuler,
Proceedings of the 17th International Conference on Computational Linguistics and the 36th Annual Meeting of the Association for Computational Linguistics (COLING/ACL'98), pages 1176–1182, Montreal, Canada, 1998.