Byung-Doh Oh

I'm a Ph.D. candidate in the Department of Linguistics at The Ohio State University working with Professor William Schuler. My work primarily focuses on the computational modeling of human sentence processing and neural network interpretability. In September 2024, I will be starting as a faculty fellow at the Center for Data Science at New York University.

Education

2024.8 Ph.D. in Linguistics, The Ohio State University

2018 M.A. in English Language Education, Seoul National University

2016 B.A. in English Language Education (summa cum laude), Seoul National University

Publications

Oh, B.-D., Yue, S., & Schuler, W. (2024). Frequency explains the inverse correlation of large language models' size, training data amount, and surprisal's fit to reading times. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics, 2644-2663.

Oh, B.-D., & Schuler, W. (2023). Transformer-based language model surprisal predicts human reading times best with about two billion training tokens. Findings of the Association for Computational Linguistics: EMNLP 2023, 1915-1921.

Oh, B.-D., & Schuler, W. (2023). Token-wise decomposition of autoregressive language model hidden states for analyzing model predictions. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, 10105-10117.

Oh, B.-D., & Schuler, W. (2023). Why does surprisal from larger Transformer-based language models provide a poorer fit to human reading times? Transactions of the Association for Computational Linguistics, 11, 336-350.

Oh, B.-D., & Schuler, W. (2022). Entropy- and distance-based predictors from GPT-2 attention patterns predict reading times over and above GPT-2 surprisal. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 9324-9334.

Oh, B.-D., Clark, C., & Schuler, W. (2022). Comparison of structural parsers and neural language models as surprisal estimators. Frontiers in Artificial Intelligence, 5, 777963.

Jin, L., Oh, B.-D., & Schuler, W. (2021). Character-based PCFG induction for modeling the syntactic acquisition of morphologically rich languages. Findings of the Association for Computational Linguistics: EMNLP 2021, 4367-4378.

Jaffe, E., Oh, B.-D., & Schuler, W. (2021). Coreference-aware surprisal predicts brain response. Findings of the Association for Computational Linguistics: EMNLP 2021, 3351-3356.

Oh, B.-D., Clark, C., & Schuler, W. (2021). Surprisal estimators for human reading times need character models. Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 3746-3757.

Oh, B.-D., & Schuler, W. (2021). Contributions of propositional content and syntactic category information in sentence processing. Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, 241-250.

Oh, B.-D. (2021). Team Ohio State at CMCL 2021 Shared Task: Fine-tuned RoBERTa for eye-tracking data prediction. Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, 97-101.

Oh, B.-D., Maneriker, P., & Jiang, N. [joint first authors] (2019). THOMAS: The hegemonic OSU morphological analyzer using seq2seq. Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology, 80-86.

Elsner, M., Sims, A. D., Erdmann, A., Hernandes, A., Jaffe, E., Jin, L., Johnson, M. B., Karim, S., King, D. L., Lamberti Nunes, L., Oh, B.-D., Rasmussen, N., Shain, C., Antetomaso, S., Dickinson, K. V., Diewald, N., McKenzie, M., & Stevens-Guille, S. (2019). Modeling morphological learning, typology, and change: What can the neural sequence-to-sequence framework contribute? Journal of Language Modelling, 7(1), 53-98.

Oh, B.-D., & So, Y. (2018). Exploring English online research and comprehension strategies of Korean college students. English Teaching, 73(3), 53-76.

Oh, B.-D. (2017). Predicting L2 writing proficiency with computational indices based on n-grams. Foreign Language Education Research, 21, 1-20.

Presentations

Oh, B.-D. (2023). The bigger-is-worse effects of model size and training data of large language model surprisal on human reading times. Invited talk at New York University, New York, NY.

Oh, B.-D., & Schuler, W. (2023). On the bigger-is-worse nature of pre-trained language model surprisal. Poster presented at the 36th Annual Conference on Human Sentence Processing, Pittsburgh, PA.

Oh, B.-D., & Schuler, W. (2023). Memory-based predictors from GPT-2 attention predict reading times over surprisal. Poster presented at the 36th Annual Conference on Human Sentence Processing, Pittsburgh, PA.

Oh, B.-D. (2022). Unified unsupervised grammar induction for typologically diverse languages. Poster presented at the DARPA Risers program, Columbus, OH.

Oh, B.-D. (2022). Character-based PCFG induction for modeling the syntactic acquisition of morphologically rich languages. Talk presented at The Ohio State University Linguistics Colloquiumfest, Columbus, OH.

Oh, B.-D. (2022). Computational models of sentence processing and syntactic acquisition. Invited talk at Dongguk University, Seoul, Korea (online).

Oh, B.-D., Clark, C., & Schuler, W. (2021). Comparison of structural and neural language models as surprisal estimators. Short talk presented at the 34th Annual CUNY Conference on Human Sentence Processing, Philadelphia, PA (online).

Oh, B.-D., & Schuler, W. (2021). Contributions of propositional content and syntactic categories in sentence processing. Short talk presented at the 34th Annual CUNY Conference on Human Sentence Processing, Philadelphia, PA (online).

Oh, B.-D. (2020). Incremental parsing for semantically-sensitive psycholinguistic predictors. Talk presented at The Ohio State University Linguistics Colloquiumfest, Columbus, OH.

Jaffe, E., & Oh, B.-D. (2019). The role of learnability in morphological change: A computational approach (presented by Evan Jaffe). Talk presented at the Fourth American International Morphology Meeting (AIMM4), Stony Brook, NY.

Teaching

2024 Invited Lecture, Guessing meaning and breaking it down (with NACLO problems), Metro Early College High School

AU20-SP21 Instructor, Linguistics 3801: Codes and Code Breaking, The Ohio State University

SU17 Teaching Assistant, College English 1, Seoul National University

Honors and Awards

2022 Selection as DARPA Riser, Defense Advanced Research Projects Agency

2018-2020 Fulbright Graduate Study Award, United States Department of State

2016 Graduate School of English Language Education Alumni Association Scholarship, Seoul National University

2015 Chunjae Education Group Scholarship, Chunjae Education Group

2014 Dept. of English Language Education Alumni Association Scholarship, Seoul National University

2011 College of Education Alumni Association Scholarship, Seoul National University