NLPExplorer

Byung Doh Oh

Number of Papers:- 12

Number of Citations:- 0

First ACL Paper:- 2019

Latest ACL Paper:- 2024

Venues:-

NAACL

EMNLP

Findings

CMCL

TACL

ACL

EACL

IJCNLP

Co-Authors:-

Similar Authors:-

Frequency Explains the Inverse Correlation of Large Language Models’ Size, Training Data Amount, and Surprisal’s Fit to Reading Times EACL

Byung-Doh Oh | Shisen Yue | William Schuler |

Leading Whitespaces of Language Models’ Subword Vocabulary Pose a Confound for Calculating Word Probabilities EMNLP

Byung-Doh Oh | William Schuler |

Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times? TACL

Byung-Doh Oh | William Schuler |

Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens F i n d i n g s - E M N L P

Byung-Doh Oh | William Schuler |

Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions ACL

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

Byung-Doh Oh | William Schuler |

Contributions of Propositional Content and Syntactic Category Information in Sentence Processing CMCL NAACL

Byung-Doh Oh | William Schuler |

Team Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye-Tracking Data Prediction CMCL NAACL

Byung-Doh Oh |

Surprisal Estimators for Human Reading Times Need Character Models ACL IJCNLP

Byung-Doh Oh | Christian Clark | William Schuler |

Coreference-aware Surprisal Predicts Brain Response EMNLP Findings

Evan Jaffe | Byung-Doh Oh | William Schuler |

Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages EMNLP Findings

Lifeng Jin | Byung-Doh Oh | William Schuler |

THOMAS: The Hegemonic OSU Morphological Analyzer using Seq2seq ACL WS

Byung-Doh Oh | Pranav Maneriker | Nanjiang Jiang |