Marten van Schijndel

发表

Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics, CMCL@NAACL-HLT 2015, Denver, CO, USA, June 4, 2015

Tim O'Donnell, Marten van Schijndel, 2015, CMCL@NAACL-HLT.

Quantity doesn’t buy quality syntax with neural language models pdf

Tal Linzen, Marten van Schijndel, Aaron Mueller, 2019, EMNLP.

Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics, CMCL 2018, Salt Lake City, Utah, USA, January 7, 2018

Tal Linzen, Marten van Schijndel, Asad Sayeed, 2018, CMCL.

Incremental Semantic Dependency Parsing

William Schuler, Marten van Schijndel, William Schuler, 2013 .

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

William Schuler, Evelina Fedorenko, Cory Shain, 2019, Neuropsychologia.

Using Priming to Uncover the Organization of Syntactic Representations in Neural Language Models

Tal Linzen, Marten van Schijndel, Grusha Prasad, 2019, CoNLL.

Prediction- and Recall-Defined Online Complexity Metrics

Marten van Schijndel, 2012 .

Prediction in the language network is sensitive to syntactic structure

Evelina Fedorenko, Cory Shain, Marten van Schijndel, 2020 .

Using syntactic priming to investigate how recurrent neural networks represent syntax

Tal Linzen, Marten van Schijndel, Grusha Prasad, 2019 .

Syntactic Probability Estimation using a Cognitively Motivated Grammar

Evan Jaffe, William Schuler, Marten van Schijndel, 2013 .

All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality pdf

Marten van Schijndel, William Timkey, William Timkey, 2021, EMNLP.

Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning

Marten van Schijndel, Forrest Davis, Forrest Davis, 2021, ACL.

Evidence of semantic processing difficulty in naturalistic reading

William Schuler, Richard Futrell, Evelina Fedorenko, 2018 .

Salience and Attention in Surprisal-Based Accounts of Language Processing

Marten van Schijndel, V. Demberg, J. Vogels, 2016, Front. Psychol..

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

William Schuler, Evelina Fedorenko, Cory Shain, 2020, Neuropsychologia.

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension

Marten van Schijndel, Evelina Fedorenko, William Schuler, 2019, Neuropsychologia.

Investigating locality effects and surprisal in written English syntactic choice phenomena

Michael White, William Schuler, Marten van Schijndel, 2016, Cognition.

Edinburgh Research Explorer Modeling bilingual word associations as connected monolingual networks

Marten van Schijndel, Tal Linzen, A. Sayeed, 2017 .

Interaction with Context During Recurrent Neural Network Sentence Processing

Marten van Schijndel, Forrest Davis, 2020, CogSci.

Dual Mechanism Priming Effects in Hindi Word Order

Marten van Schijndel, Rajakrishnan Rajkumar, Sumeet Agarwal, 2022, AACL.

Discourse Context Predictability Effects in Hindi Word Order

Marten van Schijndel, Rajakrishnan Rajkumar, Sumeet Agarwal, 2022, EMNLP.

Deep Syntactic Annotations for Broad-Coverage Psycholinguistic Modeling

Marten van Schijndel, William Schuler, Cory Shain, 2018 .

Retrieving structures from memory causes difficulty during incremental processing

Marten van Schijndel, E. Gibson, William Schuler, 2017 .

Exploring memory and processing through a gold standard annotation of Dundee

Marten van Schijndel, E. Gibson, William Schuler, 2016 .

Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment pdf

Forrest Davis, Marten van Schijndel, Marten van Schijndel, 2020, ACL.

Discourse structure interacts with reference but not syntax in neural language models pdf

Forrest Davis, Marten van Schijndel, Marten van Schijndel, 2020, CONLL.

Recurrent neural networks use discourse context in human-like garden path alleviation

Forrest Davis, Marten van Schijndel, Marten van Schijndel, 2020 .

Can Entropy Explain Successor Surprisal Effects in Reading? pdf

Tal Linzen, Marten Van Schijndel, Marten van Schijndel, 2018, ArXiv.

A Neural Model of Adaptation in Reading

Tal Linzen, Marten Van Schijndel, Marten van Schijndel, 2018, EMNLP.

Modeling garden path effects without explicit hierarchical syntax

Tal Linzen, Marten Van Schijndel, Marten van Schijndel, 2018, CogSci.

Connectionist-Inspired Incremental PCFG Parsing

William Schuler, Marten Van Schijndel, Marten van Schijndel, 2012, CMCL@NAACL-HLT.

To Point or Not to Point: Understanding How Abstractive Summarizers Paraphrase Text

Marten Van Schijndel, Marten van Schijndel, Matt Wilber, 2021, FINDINGS.

Filler-gaps that neural networks fail to generalize

Marten Van Schijndel, Marten van Schijndel, Debasmita Bhattacharya, 2020, CONLL.

Evidence of syntactic working memory usage in MEG data

William Schuler, Brian Murphy, Marten Van Schijndel, 2015, CMCL@NAACL-HLT.

Frequency effects in the processing of unbounded dependencies

William Schuler, Peter W. Culicover, Marten Van Schijndel, 2014, CogSci.

A Model of Language Processing as Hierarchic Sequential Prediction

William Schuler, Marten Van Schijndel, Marten van Schijndel, 2013, Top. Cogn. Sci..

Accurate Unbounded Dependency Recovery using Generalized Categorial Grammars

William Schuler, Luan Nguyen, Marten Van Schijndel, 2012, COLING.

Approximations of Predictive Entropy Correlate with Reading Times

William Schuler, Marten Van Schijndel, Marten van Schijndel, 2017, CogSci.

Memory access during incremental sentence processing causes reading time latency

William Schuler, Richard Futrell, Edward Gibson, 2016, CL4LC@COLING 2016.

An Analysis of Frequency- and Memory-Based Processing Costs

William Schuler, Marten Van Schijndel, Marten van Schijndel, 2013, NAACL.

Bootstrapping into Filler-Gap: An Acquisition Story

Micha Elsner, Marten Van Schijndel, Marten van Schijndel, 2014, ACL.

Hierarchic syntax improves reading time prediction

William Schuler, Marten Van Schijndel, Marten van Schijndel, 2015, NAACL.

Addressing surprisal deficiencies in reading time models

William Schuler, Marten Van Schijndel, Marten van Schijndel, 2016, CL4LC@COLING 2016.

AZMAT: Sentence Similarity Using Associative Matrices

David King, Evan Jaffe, Marten Van Schijndel, 2015, *SEMEVAL.

An Analysis of Memory-based Processing Costs using Incremental Deep Syntactic Dependency Parsing

William Schuler, Luan Nguyen, Marten Van Schijndel, 2013, CMCL.

Screening for Alzheimer ’ s with psycholinguistics

Kathleen C. Fraser, Marten van Schijndel, 2016 .