Faculty

Institution

Unit

Venue

Byung-Doh Oh

Faculty Fellow

Ohio State University

Affiliations

College of Arts and Sciences

Linguistics

Byung-doh Oh is currently serving as a College Of Arts And Sciences Faculty Fellow at Ohio State University.

Honors

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Experiences

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Education

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Byung-Doh Oh

Ohio State University

Faculty Fellow

You can click a keyword to filter the displayed items

linguistics natural language processing machine learning computer science neuroscience morphology computational linguistics reading research surprisal estimators statistics artificial intelligence computational neuroscience attention eye tracking computational intelligence language comprehension structural parsers neural language models morphological learning human reading times character models distance-based predictors fine-tuned roberta team ohio state eye-tracking data prediction character-based pcfg induction syntactic acquisition syntactic category information sentence processing propositional content token-wise decomposition language language learning neural networks psycholinguistics language recognition language science neurolinguistics language processing in the brain

Notable Scholarly Works

Notable Federal Grants

Collaborators

Token-Wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions. (2023)

Byung-Doh OhWilliam Schuler

token-wise decompositionlinguisticsmodel predictionscomputational linguisticsmachine learning

Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?. Transactions of the Association for Computational Linguistics (2023)

Byung-Doh OhWilliam Schuler

computer sciencelinguisticslanguage comprehensioncomputational linguisticsreading researchnatural language processing

Comparison of Structural Parsers and Neural Language Models as Surprisal Estimators. Frontiers in Artificial Intelligence (2022)

Byung-Doh OhChristian ClarkWilliam Schuler

artificial intelligencecomputer sciencestructural parserssurprisal estimatorsneural language modelscomputational intelligencenatural language processingmachine learning

Entropy- and Distance-Based Predictors From Gpt-2 Attention Patterns Predict Reading Times Over and Above Gpt-2 Surprisal. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2022)

Byung-Doh OhWilliam Schuler

distance-based predictorslinguisticsattentionreading researchmachine learning

Character-Based Pcfg Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages. (2021)

Lifeng JinByung-Doh OhWilliam Schuler

computer sciencecharacter-based pcfg inductionlinguisticssyntactic acquisitionnatural language processingmachine learning

Contributions of Propositional Content and Syntactic Category Information in Sentence Processing. (2021)

Byung-Doh OhWilliam Schuler

syntactic category informationlinguisticssentence processingnatural language processingpropositional content

Coreference-Aware Surprisal Predicts Brain Response. Empirical Methods in Natural Language Processing (2021)

Evan JaffeByung-Doh OhWilliam Schuler

coreference-aware surprisalcognitive neuroscienceneurosciencecognitive sciencenatural language processingbrain response

Surprisal Estimators for Human Reading Times Need Character Models. Annual Meeting of the Association for Computational Linguistics (2021)

Byung-Doh OhChristian ClarkWilliam Schuler

surprisal estimatorshuman reading timesmachine learningcharacter modelsstatistics

Team Ohio State at Cmcl 2021 Shared Task: Fine-Tuned Roberta for Eye-Tracking Data Prediction. (2021)

Byung-Doh Oh

team ohio stateeye-tracking data predictionfine-tuned robertaeye tracking

Modeling Morphological Learning, Typology, and Change: What Can the Neural Sequence-To-Sequence Framework Contribute?. Journal of Language Modelling (2019)

Micha ElsnerAndrea D. SimsAlexander ErdmannAntonio HernandezEvan JaffeLifeng JinMartha Booker JohnsonShuan KarimDavid L. KingLuana Lamberti NunesByung-Doh OhNathan RasmussenCory ShainStephanie AntetomasoKendra V. DickinsonNoah DiewaldMichelle McKenzieSymon Stevens-Guille

morphologyneurosciencecomputational neurosciencenatural language processingmorphological learning

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Byung-Doh Oh

Faculty Fellow

Ohio State University

Affiliations

College of Arts and Sciences

Linguistics

Byung-doh Oh is currently serving as a College Of Arts And Sciences Faculty Fellow at Ohio State University.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Byung-Doh Oh

Ohio State University

Faculty Fellow

You can click a keyword to filter the displayed items

Notable Scholarly Works

Notable Federal Grants

Collaborators

Token-Wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions. (2023)

Byung-Doh OhWilliam Schuler

token-wise decompositionlinguisticsmodel predictionscomputational linguisticsmachine learning

Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?. Transactions of the Association for Computational Linguistics (2023)

Byung-Doh OhWilliam Schuler

computer sciencelinguisticslanguage comprehensioncomputational linguisticsreading researchnatural language processing

Comparison of Structural Parsers and Neural Language Models as Surprisal Estimators. Frontiers in Artificial Intelligence (2022)

Byung-Doh OhChristian ClarkWilliam Schuler

artificial intelligencecomputer sciencestructural parserssurprisal estimatorsneural language modelscomputational intelligencenatural language processingmachine learning

Byung-Doh OhWilliam Schuler

distance-based predictorslinguisticsattentionreading researchmachine learning

Character-Based Pcfg Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages. (2021)

Lifeng JinByung-Doh OhWilliam Schuler

computer sciencecharacter-based pcfg inductionlinguisticssyntactic acquisitionnatural language processingmachine learning

Contributions of Propositional Content and Syntactic Category Information in Sentence Processing. (2021)

Byung-Doh OhWilliam Schuler

syntactic category informationlinguisticssentence processingnatural language processingpropositional content

Coreference-Aware Surprisal Predicts Brain Response. Empirical Methods in Natural Language Processing (2021)

Evan JaffeByung-Doh OhWilliam Schuler

coreference-aware surprisalcognitive neuroscienceneurosciencecognitive sciencenatural language processingbrain response

Surprisal Estimators for Human Reading Times Need Character Models. Annual Meeting of the Association for Computational Linguistics (2021)

Byung-Doh OhChristian ClarkWilliam Schuler

surprisal estimatorshuman reading timesmachine learningcharacter modelsstatistics

Team Ohio State at Cmcl 2021 Shared Task: Fine-Tuned Roberta for Eye-Tracking Data Prediction. (2021)

Byung-Doh Oh

team ohio stateeye-tracking data predictionfine-tuned robertaeye tracking

Modeling Morphological Learning, Typology, and Change: What Can the Neural Sequence-To-Sequence Framework Contribute?. Journal of Language Modelling (2019)

morphologyneurosciencecomputational neurosciencenatural language processingmorphological learning

1
2
10 / page

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language technologies

cognitive computing

Nicole Y. Wicha

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University of Texas at San Antonio

cognitive linguistics

human electrophysiology

Carrie Jackson

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Pennsylvania State University

language acquisition

secondlanguage acquisition

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neuroscience

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Reiko Mazuka

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