no code implementations • Findings (EMNLP) 2021 • Evan Jaffe, Byung-Doh Oh, William Schuler
Recent evidence supports a role for coreference processing in guiding human expectations about upcoming words during reading, based on covariation between reading times and word surprisal estimated by a coreference-aware semantic processing model (Jaffe et al. 2020). The present study reproduces and elaborates on this finding by (1) enabling the parser to process subword information that might better approximate human morphological knowledge, and (2) extending evaluation of coreference effects from self-paced reading to human brain imaging data.
no code implementations • Findings (EMNLP) 2021 • Lifeng Jin, Byung-Doh Oh, William Schuler
A subsequent evaluation on multilingual treebanks shows that the model with subword information achieves state-of-the-art results on many languages, further supporting a distributional model of syntactic acquisition.
1 code implementation • NAACL (CMCL) 2021 • Byung-Doh Oh
This paper describes Team Ohio State’s approach to the CMCL 2021 Shared Task, the goal of which is to predict five eye-tracking features from naturalistic self-paced reading corpora.
no code implementations • NAACL (CMCL) 2021 • Byung-Doh Oh, William Schuler
Expectation-based theories of sentence processing posit that processing difficulty is determined by predictability in context.
1 code implementation • 3 Feb 2024 • Byung-Doh Oh, Shisen Yue, William Schuler
Additionally, training dynamics reveal that during later training steps, all model variants learn to predict rare words and that larger model variants do so more accurately, which explains the detrimental effect of both training data amount and model size on fit to reading times.
1 code implementation • 17 May 2023 • Byung-Doh Oh, William Schuler
While there is much recent interest in studying why Transformer-based large language models make predictions the way they do, the complex computations performed within each layer have made their behavior somewhat opaque.
no code implementations • 22 Apr 2023 • Byung-Doh Oh, William Schuler
Recent psycholinguistic studies have drawn conflicting conclusions about the relationship between the quality of a language model and the ability of its surprisal estimates to predict human reading times, which has been speculated to be due to the large gap in both the amount of training data and model capacity across studies.
no code implementations • 23 Dec 2022 • Byung-Doh Oh, William Schuler
This work presents a detailed linguistic analysis into why larger Transformer-based pre-trained language models with more parameters and lower perplexity nonetheless yield surprisal estimates that are less predictive of human reading times.
1 code implementation • 21 Dec 2022 • Byung-Doh Oh, William Schuler
Transformer-based large language models are trained to make predictions about the next word by aggregating representations of previous tokens through their self-attention mechanism.
1 code implementation • ACL 2021 • Byung-Doh Oh, Christian Clark, William Schuler
While the use of character models has been popular in NLP applications, it has not been explored much in the context of psycholinguistic modeling.
no code implementations • WS 2019 • Byung-Doh Oh, Pranav Maneriker, Nanjiang Jiang
This paper describes the OSU submission to the SIGMORPHON 2019 shared task, Crosslinguality and Context in Morphology.