Restrictions on Tree Adjoining Languages. - Giorgio Satta; William Schuler (COLING-ACL, 1998)

Exploiting semantic dependencies in parsing. - William Schuler (TAG+, 1998)

Preserving Semantic Dependencies in Synchronous Tree Adjoining Grammar. - William Schuler (ACL, 1999)

Multi-Component TAG and Notions of Formal Power. - William Schuler; David Chiang 001; Mark Dras (ACL, 2000)

Dynamically altering agent behaviors using natural language instructions. - Rama Bindiganavale; William Schuler; Jan M. Allbeck; Norman I. Badler; Aravind K. Joshi; Martha Stone Palmer (Agents, 2000)

A Machine Translation System from English to American Sign Language. - Liwei Zhao; Karin Kipper; William Schuler; Christian Vogler; Norman I. Badler; Martha Stone Palmer (AMTA, 2000)

Some remarks on an extension of synchronous TAG. - David Chiang 001; William Schuler; Mark Dras (TAG+, 2000)

Building a class-based verb lexicon using TAGs. - Karin Kipper; Hoa Trang Dang; William Schuler; Martha Palmer (TAG+, 2000)

Computational Properties of Environment-based Disambiguation. - William Schuler (ACL, 2001)

ACUMEN: amplifying control and understanding of multiple entities. - Jan M. Allbeck; Karin Kipper; Charles Adams; William Schuler; Elena Zoubanova; Norman I. Badler; Martha Stone Palmer; Aravind K. Joshi (AAMAS, 2002)

Interleaved Semantic Interpretation in Environment-based Parsing. - William Schuler (COLING, 2002)

Using Model-Theoretic Semantic Interpretation to Guide Statistical Parsing and Word Recognition in a Spoken Language Interface. - William Schuler (ACL, 2003)

Using Volunteers to Annotate Biomedical Corpora for Anaphora Resolution. - Shana Watters; Brian McInnes; David McKoskey; Tim Miller 002; Daniel Boley; Maria L. Gini; William Schuler; A. Polukeyeva; Jeanette K. Gundel; Sergey V. Pakhomov; Guergana Savova (AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors, 2005)

Exploiting a sensed environment to improve human-agent communication. - Shana Watters; Tim Miller 002; Praveen Balachandran; William Schuler; Richard M. Voyles (AAMAS, 2005)

Integrating denotational meaning into a DBN language model. - William Schuler; Tim Miller 002 (INTERSPEECH, 2005)

Dynamic evidence models in a DBN phone recognizer. - William Schuler; Tim Miller 002; Stephen T. Wu; Andrew Exley (INTERSPEECH, 2006)

Elements of a spoken language programming interface for robots. - Tim Miller 002; Andrew Exley; William Schuler (HRI, 2007)

A Unified Syntactic Model for Parsing Fluent and Disfluent Speech. - Tim Miller 002; William Schuler (ACL (2), 2008)

A Syntactic Time-Series Model for Parsing Fluent and Disfluent Speech. - Tim Miller 002; William Schuler (COLING, 2008)

Toward a Psycholinguistically-Motivated Model of Language Processing. - William Schuler; Samir AbdelRahman; Tim Miller 002; Lane Schwartz (COLING, 2008)

Referential semantic language modeling for data-poor domains. - Stephen T. Wu; Lane Schwartz; William Schuler (ICASSP, 2008)

Exploiting referential context in spoken language interfaces for data-poor domains. - Stephen T. Wu; Lane Schwartz; William Schuler (IUI, 2008)

Parsing Speech Repair without Specialized Grammar Symbols. - Tim Miller 002; Luan Nguyen; William Schuler (ACL/IJCNLP (2), 2009)

Positive effects of redundant descriptions in an interactive semantic speech interface. - Lane Schwartz; Luan Nguyen; Andrew Exley; William Schuler (IUI, 2009)

Positive Results for Parsing with a Bounded Stack using a Model-Based Right-Corner Transform. - William Schuler (HLT-NAACL, 2009)

A Framework for Fast Incremental Interpretation during Speech Decoding. - William Schuler; Stephen T. Wu; Lane Schwartz (Comput. Linguistics, 2009)

HHMM Parsing with Limited Parallelism. - Tim Miller 002; William Schuler (CMCL@ACL, 2010)

Complexity Metrics in an Incremental Right-Corner Parser. - Stephen T. Wu; Asaf Bachrach; Carlos Cardenas; William Schuler (ACL, 2010)

Incremental Parsing in Bounded Memory. - William Schuler (TAG, 2010)

Broad-Coverage Parsing Using Human-Like Memory Constraints. - William Schuler; Samir AbdelRahman; Tim Miller 002; Lane Schwartz (Comput. Linguistics, 2010)

A Pronoun Anaphora Resolution System based on Factorial Hidden Markov Models. - Dingcheng Li; Tim Miller 002; William Schuler (ACL, 2011)

Incremental Syntactic Language Models for Phrase-based Translation. - Lane Schwartz; Chris Callison-Burch; William Schuler; Stephen T. Wu (ACL, 2011)

Effects of Filler-gap Dependencies Working Memory Requirements for Parsing. - William Schuler (CogSci, 2011)

Structured Composition of Semantic Vectors. - Stephen T. Wu; William Schuler (IWCS, 2011)

Tree-Rewriting Models of Multi-Word Expressions. - William Schuler; Aravind K. Joshi (MWE@ACL, 2011)

Connectionist-Inspired Incremental PCFG Parsing. - Marten van Schijndel; Andrew Exley; William Schuler (CMCL@NAACL-HLT, 2012)

Accurate Unbounded Dependency Recovery using Generalized Categorial Grammars. - Luan Nguyen; Marten van Schijndel; William Schuler (COLING, 2012)

An Analysis of Memory-based Processing Costs using Incremental Deep Syntactic Dependency Parsing. - Marten van Schijndel; Luan Nguyen; William Schuler (CMCL, 2013)

An Analysis of Frequency- and Memory-Based Processing Costs. - Marten van Schijndel; William Schuler (HLT-NAACL, 2013)

A Model of Language Processing as Hierarchic Sequential Prediction. - Marten van Schijndel; Andrew Exley; William Schuler (Top. Cogn. Sci., 2013)

Sentence Processing in a Vectorial Model of Working Memory. - William Schuler (CMCL@ACL, 2014)

Frequency effects in the processing of unbounded dependencies. - Marten van Schijndel; William Schuler; Peter W. Culicover (CogSci, 2014)

Cognitive Compositional Semantics using Continuation Dependencies. - William Schuler; Adam Wheeler (*SEM@COLING, 2014)

Evidence of syntactic working memory usage in MEG data. - Marten van Schijndel; Brian Murphy; William Schuler (CMCL@NAACL-HLT, 2015)

Interpreting Questions with a Log-Linear Ranking Model in a Virtual Patient Dialogue System. - Evan Jaffe; Michael White 001; William Schuler; Eric Fosler-Lussier; Alex Rosenfeld; Douglas Danforth (BEA@NAACL-HLT, 2015)

A Comparison of Word Similarity Performance Using Explanatory and Non-explanatory Texts. - Lifeng Jin; William Schuler (HLT-NAACL, 2015)

Hierarchic syntax improves reading time prediction. - Marten van Schijndel; William Schuler (HLT-NAACL, 2015)

Addressing surprisal deficiencies in reading time models. - Marten van Schijndel; William Schuler (CL4LC@COLING 2016, 2016)

Memory access during incremental sentence processing causes reading time latency. - Cory Shain; Marten van Schijndel; Richard Futrell; Edward Gibson; William Schuler (CL4LC@COLING 2016, 2016)

Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input. - Cory Shain; William Bryce; Lifeng Jin; Victoria Krakovna; Finale Doshi-Velez; Timothy Miller 002; William Schuler; Lane Schwartz (COLING, 2016)

OSU_CHGCG at SemEval-2016 Task 9 : Chinese Semantic Dependency Parsing with Generalized Categorial Grammar. - Manjuan Duan; Lifeng Jin; William Schuler (SemEval@NAACL-HLT, 2016)

OCLSP at SemEval-2016 Task 9: Multilayered LSTM as a Neural Semantic Dependency Parser. - Lifeng Jin; Manjuan Duan; William Schuler (SemEval@NAACL-HLT, 2016)

Approximations of Predictive Entropy Correlate with Reading Times. - Marten van Schijndel; William Schuler (CogSci, 2017)

Coreference and Focus in Reading Times. - Evan Jaffe; Cory Shain; William Schuler (CMCL, 2018)

Depth-bounding is effective: Improvements and Evaluation of Unsupervised PCFG Induction. - Lifeng Jin; Finale Doshi-Velez; Timothy Miller 002; William Schuler; Lane Schwartz (EMNLP, 2018)

Deconvolutional time series regression: A technique for modeling temporally diffuse effects. - Cory Shain; William Schuler (EMNLP, 2018)

Test Sets for Chinese Nonlocal Dependency Parsing. - Manjuan Duan; William Schuler (LREC, 2018)

Unsupervised Grammar Induction with Depth-bounded PCFG. - Lifeng Jin; Finale Doshi-Velez; Timothy Miller 002; William Schuler; Lane Schwartz (Trans. Assoc. Comput. Linguistics, 2018)

Unsupervised Learning of PCFGs with Normalizing Flow. - Lifeng Jin; Finale Doshi-Velez; Timothy Miller 002; Lane Schwartz; William Schuler (ACL (1), 2019)

Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction. - Lifeng Jin; William Schuler (ACL (1), 2019)

Coreference information guides human expectations during natural reading. - Evan Jaffe; Cory Shain; William Schuler (COLING, 2020)

Grounded PCFG Induction with Images. - Lifeng Jin; William Schuler (AACL/IJCNLP, 2020)

Memory-bounded Neural Incremental Parsing for Psycholinguistic Prediction. - Lifeng Jin; William Schuler (IWPT 2020, 2020)

The Importance of Category Labels in Grammar Induction with Child-directed Utterances. - Lifeng Jin; William Schuler (IWPT 2020, 2020)

A Corpus of Encyclopedia Articles with Logical Forms. - Nathan Rasmussen; William Schuler (LREC, 2020)

Contributions of Propositional Content and Syntactic Category Information in Sentence Processing. - Byung-Doh Oh; William Schuler (CMLS, 2021)

Surprisal Estimators for Human Reading Times Need Character Models. - Byung-Doh Oh; Christian Clark; William Schuler (ACL/IJCNLP (1), 2021)

Coreference-aware Surprisal Predicts Brain Response. - Evan Jaffe; Byung-Doh Oh; William Schuler (EMNLP (Findings), 2021)

Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages. - Lifeng Jin; Byung-Doh Oh; William Schuler (EMNLP (Findings), 2021)

Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition. - Lifeng Jin; Lane Schwartz; Finale Doshi-Velez; Timothy Miller 002; William Schuler (Comput. Linguistics, 2021)

Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal. - Byung-Doh Oh; William Schuler (EMNLP, 2022)

A Deep Learning Approach to Analyzing Continuous-Time Systems. - Cory Shain; William Schuler (CoRR, 2022)

Comparison of Structural Parsers and Neural Language Models as Surprisal Estimators. - Byung-Doh Oh; Christian Clark; William Schuler (Frontiers Artif. Intell., 2022)

Categorial grammar induction from raw data. - Christian Clark; William Schuler (ACL (Findings), 2023)

Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions. - Byung-Doh Oh; William Schuler (ACL (1), 2023)

Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens. - Byung-Doh Oh; William Schuler (EMNLP (Findings), 2023)

Bootstrapping a Conversational Guide for Colonoscopy Prep. - Pulkit Arya; Madeleine Bloomquist; Subhankar Chakraborty; Andrew Perrault; William Schuler; Eric Fosler-Lussier; Michael White 001 (SIGDIAL, 2023)

Transformer-Based LM Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens. - Byung-Doh Oh; William Schuler (CoRR, 2023)

Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times? - Byung-Doh Oh; William Schuler (Trans. Assoc. Comput. Linguistics, 2023)

Categorial Grammar Induction with Stochastic Category Selection. - Christian Clark; William Schuler (LREC/COLING, 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; William Schuler (EACL (1), 2024)

Leading Whitespaces of Language Models' Subword Vocabulary Pose a Confound for Calculating Word Probabilities. - Byung-Doh Oh; William Schuler (EMNLP, 2024)

Leading Whitespaces of Language Models' Subword Vocabulary Poses a Confound for Calculating Word Probabilities. - Byung-Doh Oh; William Schuler (CoRR, 2024)

Evaluation of an Algorithmic-Level Left-Corner Parsing Account of Surprisal Effects. - William Schuler; Shisen Yue (Cogn. Sci., 2024)

The Impact of Token Granularity on the Predictive Power of Language Model Surprisal. - Byung-Doh Oh; William Schuler (ACL (1), 2025)

The Inverse Scaling Effect of Pre-Trained Language Model Surprisal Is Not Due to Data Leakage. - Byung-Doh Oh; Hongao Zhu; William Schuler (ACL (Findings), 2025)

Linear Recency Bias During Training Improves Transformers' Fit to Reading Times. - Christian Clark; Byung-Doh Oh; William Schuler (COLING, 2025)

Vectors from Larger Language Models Predict Human Reading Time and fMRI Data More Poorly when Dimensionality Expansion is Controlled. - Yi-Chien Lin; Hongao Zhu; William Schuler (CoRR, 2025)

Surprisal from Larger Transformer-based Language Models Predicts fMRI Data More Poorly. - Yi-Chien Lin; William Schuler (CoRR, 2025)

How Well Does First-Token Entropy Approximate Word Entropy as a Psycholinguistic Predictor? - Christian Clark; Byung-Doh Oh; William Schuler (CoRR, 2025)