activity
20242026
collaborators
Showing cs.CLShow all

6 papers · 1 filter

cs.CL2026

"Newspaper Eat" Means "Not Tasty": A Taxonomy and Benchmark for Coded Language in Real-World Chinese Online Reviews

Ruyuan Wan, Changye Li, Ting-Hao 'Kenneth' Huang

Coded language is an important part of human communication. It refers to cases where users intentionally encode meaning so that the surface text differs from the intended meaning a…

cs.CL2025

Mitigating Confounding in Speech-Based Dementia Detection through Weight Masking

Zhecheng Sheng, Xiruo Ding, Brian Hur +3

Deep transformer models have been used to detect linguistic anomalies in patient transcripts for early Alzheimer's disease (AD) screening. While pre-trained neural language models…

cs.CL2025

"Is There Anything Else?'': Examining Administrator Influence on Linguistic Features from the Cookie Theft Picture Description Cognitive Test

Changye Li, Zhecheng Sheng, Trevor Cohen +1

Alzheimer's Disease (AD) dementia is a progressive neurodegenerative disease that negatively impacts patients' cognitive ability. Previous studies have demonstrated that changes in…

cs.CL2025

Bigger But Not Better: Small Neural Language Models Outperform Large Language Models in Detection of Thought Disorder

Changye Li, Weizhe Xu, Serguei Pakhomov +3

Disorganized thinking is a key diagnostic indicator of schizophrenia-spectrum disorders. Recently, clinical estimates of the severity of disorganized thinking have been shown to co…

cs.CL2024

Reexamining Racial Disparities in Automatic Speech Recognition Performance: The Role of Confounding by Provenance

Changye Li, Trevor Cohen, Serguei Pakhomov

Automatic speech recognition (ASR) models trained on large amounts of audio data are now widely used to convert speech to written text in a variety of applications from video capti…

cs.CL2024

Too Big to Fail: Larger Language Models are Disproportionately Resilient to Induction of Dementia-Related Linguistic Anomalies

Changye Li, Zhecheng Sheng, Trevor Cohen +1

As artificial neural networks grow in complexity, understanding their inner workings becomes increasingly challenging, which is particularly important in healthcare applications. T…