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William Schuler
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2020 – today
- 2024
- [c73]Christian Clark, William Schuler:
Categorial Grammar Induction with Stochastic Category Selection. LREC/COLING 2024: 2893-2900 - [c72]Byung-Doh Oh, Shisen Yue, William Schuler:
Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times. EACL (1) 2024: 2644-2663 - [c71]Byung-Doh Oh, William Schuler:
Leading Whitespaces of Language Models' Subword Vocabulary Pose a Confound for Calculating Word Probabilities. EMNLP 2024: 3464-3472 - [i14]Byung-Doh Oh, Shisen Yue, William Schuler:
Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times. CoRR abs/2402.02255 (2024) - [i13]Byung-Doh Oh, William Schuler:
Leading Whitespaces of Language Models' Subword Vocabulary Poses a Confound for Calculating Word Probabilities. CoRR abs/2406.10851 (2024) - [i12]Christian Clark, Byung-Doh Oh, William Schuler:
Linear Recency Bias During Training Improves Transformers' Fit to Reading Times. CoRR abs/2409.11250 (2024) - 2023
- [j7]Byung-Doh Oh, William Schuler:
Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times? Trans. Assoc. Comput. Linguistics 11: 336-350 (2023) - [c70]Christian Clark, William Schuler:
Categorial grammar induction from raw data. ACL (Findings) 2023: 2368-2379 - [c69]Byung-Doh Oh, William Schuler:
Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions. ACL (1) 2023: 10105-10117 - [c68]Byung-Doh Oh, William Schuler:
Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens. EMNLP (Findings) 2023: 1915-1921 - [c67]Pulkit Arya, Madeleine Bloomquist, Subhankar Chakraborty, Andrew Perrault, William Schuler, Eric Fosler-Lussier, Michael White:
Bootstrapping a Conversational Guide for Colonoscopy Prep. SIGDIAL 2023: 413-420 - [i11]Byung-Doh Oh, William Schuler:
Transformer-Based LM Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens. CoRR abs/2304.11389 (2023) - [i10]Byung-Doh Oh, William Schuler:
Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions. CoRR abs/2305.10614 (2023) - 2022
- [j6]Byung-Doh Oh, Christian Clark, William Schuler:
Comparison of Structural Parsers and Neural Language Models as Surprisal Estimators. Frontiers Artif. Intell. 5: 777963 (2022) - [c66]Byung-Doh Oh, William Schuler:
Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal. EMNLP 2022: 9324-9334 - [i9]Cory Shain, William Schuler:
A Deep Learning Approach to Analyzing Continuous-Time Systems. CoRR abs/2209.12128 (2022) - [i8]Byung-Doh Oh, William Schuler:
Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal. CoRR abs/2212.11185 (2022) - [i7]Byung-Doh Oh, William Schuler:
Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times? CoRR abs/2212.12131 (2022) - 2021
- [j5]Lifeng Jin, Lane Schwartz
, Finale Doshi-Velez, Timothy A. Miller, William Schuler:
Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition. Comput. Linguistics 47(1): 181-216 (2021) - [c65]Byung-Doh Oh, Christian Clark, William Schuler:
Surprisal Estimators for Human Reading Times Need Character Models. ACL/IJCNLP (1) 2021: 3746-3757 - [c64]Byung-Doh Oh, William Schuler:
Contributions of Propositional Content and Syntactic Category Information in Sentence Processing. CMLS 2021: 241-250 - [c63]Evan Jaffe
Coreference-aware Surprisal Predicts Brain Response. EMNLP (Findings) 2021: 3351-3356 - [c62]Lifeng Jin, Byung-Doh Oh, William Schuler:
Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages. EMNLP (Findings) 2021: 4367-4378 - 2020
- [c61]Evan Jaffe
Coreference information guides human expectations during natural reading. COLING 2020: 4587-4599 - [c60]Lifeng Jin, William Schuler:
Grounded PCFG Induction with Images. AACL/IJCNLP 2020: 396-408 - [c59]Lifeng Jin, William Schuler:
Memory-bounded Neural Incremental Parsing for Psycholinguistic Prediction. IWPT 2020 2020: 48-61 - [c58]Lifeng Jin, William Schuler:
The Importance of Category Labels in Grammar Induction with Child-directed Utterances. IWPT 2020 2020: 145-150 - [c57]Nathan Rasmussen, William Schuler:
A Corpus of Encyclopedia Articles with Logical Forms. LREC 2020: 1051-1060 - [i6]Lifeng Jin, William Schuler:
The Importance of Category Labels in Grammar Induction with Child-directed Utterances. CoRR abs/2006.11646 (2020)
2010 – 2019
- 2019
- [c56]Lifeng Jin, Finale Doshi-Velez, Timothy A. Miller, Lane Schwartz, William Schuler:
Unsupervised Learning of PCFGs with Normalizing Flow. ACL (1) 2019: 2442-2452 - [c55]Lifeng Jin, William Schuler:
Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction. ACL (1) 2019: 2453-2463 - 2018
- [j4]Lifeng Jin, Finale Doshi-Velez, Timothy A. Miller, William Schuler, Lane Schwartz:
Unsupervised Grammar Induction with Depth-bounded PCFG. Trans. Assoc. Comput. Linguistics 6: 211-224 (2018) - [c54]Evan Jaffe
Coreference and Focus in Reading Times. CMCL 2018: 1-9 - [c53]Cory Shain, William Schuler:
Deconvolutional time series regression: A technique for modeling temporally diffuse effects. EMNLP 2018: 2679-2689 - [c52]Lifeng Jin, Finale Doshi-Velez, Timothy A. Miller, William Schuler, Lane Schwartz:
Depth-bounding is effective: Improvements and Evaluation of Unsupervised PCFG Induction. EMNLP 2018: 2721-2731 - [c51]Manjuan Duan, William Schuler:
Test Sets for Chinese Nonlocal Dependency Parsing. LREC 2018 - [i5]Lifeng Jin, Finale Doshi-Velez, Timothy A. Miller, William Schuler, Lane Schwartz:
Unsupervised Grammar Induction with Depth-bounded PCFG. CoRR abs/1802.08545 (2018) - [i4]Lifeng Jin, Finale Doshi-Velez, Timothy A. Miller, William Schuler, Lane Schwartz:
Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction. CoRR abs/1809.03112 (2018) - 2017
- [c50]Marten van Schijndel, William Schuler:
Approximations of Predictive Entropy Correlate with Reading Times. CogSci 2017 - [e1]Ted Gibson, Tal Linzen, Asad B. Sayeed, Marten van Schijndel, William Schuler:
Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics, CMCL@EACL 2017, Valencia, Spain, April 3, 2017. Association for Computational Linguistics 2017, ISBN 978-1-945626-38-8 [contents] - 2016
- [c49]Marten van Schijndel, William Schuler:
Addressing surprisal deficiencies in reading time models. CL4LC@COLING 2016 2016: 32-37 - [c48]Cory Shain, Marten van Schijndel, Richard Futrell, Edward Gibson, William Schuler:
Memory access during incremental sentence processing causes reading time latency. CL4LC@COLING 2016 2016: 49-58 - [c47]Cory Shain, William Bryce, Lifeng Jin, Victoria Krakovna, Finale Doshi-Velez, Timothy A. Miller, William Schuler, Lane Schwartz:
Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input. COLING 2016: 964-975 - [c46]Lifeng Jin, Manjuan Duan, William Schuler:
OCLSP at SemEval-2016 Task 9: Multilayered LSTM as a Neural Semantic Dependency Parser. SemEval@NAACL-HLT 2016: 1212-1217 - [c45]Manjuan Duan, Lifeng Jin, William Schuler:
OSU_CHGCG at SemEval-2016 Task 9 : Chinese Semantic Dependency Parsing with Generalized Categorial Grammar. SemEval@NAACL-HLT 2016: 1218-1224 - 2015
- [c44]Marten van Schijndel
Evidence of syntactic working memory usage in MEG data. CMCL@NAACL-HLT 2015: 79-88 - [c43]Evan Jaffe
Interpreting Questions with a Log-Linear Ranking Model in a Virtual Patient Dialogue System. BEA@NAACL-HLT 2015: 86-96 - [c42]Lifeng Jin, William Schuler:
A Comparison of Word Similarity Performance Using Explanatory and Non-explanatory Texts. HLT-NAACL 2015: 990-994 - [c41]Marten van Schijndel
Hierarchic syntax improves reading time prediction. HLT-NAACL 2015: 1597-1605 - 2014
- [c40]William Schuler:
Sentence Processing in a Vectorial Model of Working Memory. CMCL@ACL 2014: 19-27 - [c39]Marten van Schijndel, William Schuler, Peter W. Culicover:
Frequency effects in the processing of unbounded dependencies. CogSci 2014 - [c38]William Schuler, Adam Wheeler:
Cognitive Compositional Semantics using Continuation Dependencies. *SEM@COLING 2014 - 2013
- [j3]Marten van Schijndel, Andrew Exley, William Schuler:
A Model of Language Processing as Hierarchic Sequential Prediction. Top. Cogn. Sci. 5(3): 522-540 (2013) - [c37]Marten van Schijndel, Luan Nguyen, William Schuler:
An Analysis of Memory-based Processing Costs using Incremental Deep Syntactic Dependency Parsing. CMCL 2013: 37-46 - [c36]Marten van Schijndel, William Schuler:
An Analysis of Frequency- and Memory-Based Processing Costs. HLT-NAACL 2013: 95-105 - 2012
- [c35]Marten van Schijndel, Andrew Exley, William Schuler:
Connectionist-Inspired Incremental PCFG Parsing. CMCL@NAACL-HLT 2012: 51-60 - [c34]Luan Nguyen, Marten van Schijndel, William Schuler:
Accurate Unbounded Dependency Recovery using Generalized Categorial Grammars. COLING 2012: 2125-2140 - 2011
- [c33]Lane Schwartz, Chris Callison-Burch, William Schuler, Stephen T. Wu:
Incremental Syntactic Language Models for Phrase-based Translation. ACL 2011: 620-631 - [c32]Dingcheng Li, Timothy A. Miller, William Schuler:
A Pronoun Anaphora Resolution System based on Factorial Hidden Markov Models. ACL 2011: 1169-1178 - [c31]William Schuler:
Effects of Filler-gap Dependencies Working Memory Requirements for Parsing. CogSci 2011 - [c30]Stephen T. Wu, William Schuler:
Structured Composition of Semantic Vectors. IWCS 2011 - [c29]William Schuler, Aravind K. Joshi:
Tree-Rewriting Models of Multi-Word Expressions. MWE@ACL 2011: 25-30 - 2010
- [j2]William Schuler, Samir AbdelRahman
Broad-Coverage Parsing Using Human-Like Memory Constraints. Comput. Linguistics 36(1): 1-30 (2010) - [c28]Stephen T. Wu, Asaf Bachrach, Carlos Cardenas, William Schuler:
Complexity Metrics in an Incremental Right-Corner Parser. ACL 2010: 1189-1198 - [c27]Timothy A. Miller, William Schuler:
HHMM Parsing with Limited Parallelism. CMCL@ACL 2010: 27-35 - [c26]William Schuler:
Incremental Parsing in Bounded Memory. TAG 2010: 1-8
2000 – 2009
- 2009
- [j1]William Schuler, Stephen T. Wu, Lane Schwartz:
A Framework for Fast Incremental Interpretation during Speech Decoding. Comput. Linguistics 35(3): 313-343 (2009) - [c25]Timothy A. Miller, Luan Nguyen, William Schuler:
Parsing Speech Repair without Specialized Grammar Symbols. ACL/IJCNLP (2) 2009: 277-280 - [c24]Lane Schwartz, Luan Nguyen, Andrew Exley, William Schuler:
Positive effects of redundant descriptions in an interactive semantic speech interface. IUI 2009: 217-226 - [c23]William Schuler:
Positive Results for Parsing with a Bounded Stack using a Model-Based Right-Corner Transform. HLT-NAACL 2009: 344-352 - 2008
- [c22]Timothy A. Miller, William Schuler:
A Unified Syntactic Model for Parsing Fluent and Disfluent Speech. ACL (2) 2008: 105-108 - [c21]Timothy A. Miller, William Schuler:
A Syntactic Time-Series Model for Parsing Fluent and Disfluent Speech. COLING 2008: 569-576 - [c20]William Schuler, Samir AbdelRahman, Timothy A. Miller, Lane Schwartz:
Toward a Psycholinguistically-Motivated Model of Language Processing. COLING 2008: 785-792 - [c19]Stephen T. Wu, Lane Schwartz, William Schuler:
Referential semantic language modeling for data-poor domains. ICASSP 2008: 5085-5088 - [c18]Stephen T. Wu, Lane Schwartz, William Schuler:
Exploiting referential context in spoken language interfaces for data-poor domains. IUI 2008: 285-292 - 2007
- [c17]Timothy A. Miller, Andrew Exley, William Schuler:
Elements of a spoken language programming interface for robots. HRI 2007: 231-237 - 2006
- [c16]William Schuler, Timothy A. Miller, Stephen T. Wu, Andrew Exley:
Dynamic evidence models in a DBN phone recognizer. INTERSPEECH 2006 - 2005
- [c15]Shana Watters, Brian McInnes, David McKoskey, Timothy A. Miller, Daniel Boley, Maria L. Gini, William Schuler, A. Polukeyeva, Jeanette K. Gundel, Sergey V. Pakhomov, Guergana Savova:
Using Volunteers to Annotate Biomedical Corpora for Anaphora Resolution. AAAI Spring Symposium: Knowledge Collection from Volunteer Contributors 2005: 117- - [c14]Shana Watters, Timothy A. Miller, Praveen Balachandran, William Schuler, Richard M. Voyles:
Exploiting a sensed environment to improve human-agent communication. AAMAS 2005: 44-50 - [c13]William Schuler, Timothy A. Miller:
Integrating denotational meaning into a DBN language model. INTERSPEECH 2005: 901-904 - 2003
- [c12]William Schuler:
Using Model-Theoretic Semantic Interpretation to Guide Statistical Parsing and Word Recognition in a Spoken Language Interface. ACL 2003: 529-536 - 2002
- [c11]Jan M. Allbeck, Karin Kipper, Charles Adams, William Schuler, Elena Zoubanova, Norman I. Badler, Martha Stone Palmer, Aravind K. Joshi:
ACUMEN: amplifying control and understanding of multiple entities. AAMAS 2002: 191-198 - [c10]William Schuler:
Interleaved Semantic Interpretation in Environment-based Parsing. COLING 2002 - [i3]William Schuler:
Interleaved semantic interpretation in environment-based parsing. CoRR cs.CL/0206026 (2002) - 2001
- [c9]William Schuler:
Computational Properties of Environment-based Disambiguation. ACL 2001: 466-473 - [i2]William Schuler:
Computational properties of environment-based disambiguation. CoRR cs.CL/0106011 (2001) - 2000
- [c8]William Schuler, David Chiang, Mark Dras:
Multi-Component TAG and Notions of Formal Power. ACL 2000: 448-455 - [c7]Rama Bindiganavale, William Schuler, Jan M. Allbeck, Norman I. Badler, Aravind K. Joshi, Martha Stone Palmer
Dynamically altering agent behaviors using natural language instructions. Agents 2000: 293-300 - [c6]Liwei Zhao, Karin Kipper, William Schuler, Christian Vogler, Norman I. Badler, Martha Stone Palmer
A Machine Translation System from English to American Sign Language. AMTA 2000: 54-67 - [c5]David Chiang, William Schuler, Mark Dras:
Some remarks on an extension of synchronous TAG. TAG+ 2000: 61-66 - [c4]Karin Kipper, Hoa Trang Dang, William Schuler, Martha Palmer:
Building a class-based verb lexicon using TAGs. TAG+ 2000: 147-154
1990 – 1999
- 1999
- [c3]William Schuler:
Preserving Semantic Dependencies in Synchronous Tree Adjoining Grammar. ACL 1999: 88-95 - 1998
- [c2]Giorgio Satta, William Schuler:
Restrictions on Tree Adjoining Languages. COLING-ACL 1998: 1176-1182 - [c1]William Schuler:
Exploiting semantic dependencies in parsing. TAG+ 1998: 155-158 - [i1]Giorgio Satta, William Schuler:
Restrictions on Tree Adjoining Languages. CoRR cs.CL/9810015 (1998)
Coauthor Index
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last updated on 2024-11-15 19:32 CET by the dblp team
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