mlsanityData to 14 Oct 2026

Robustness and Sensitivity of BERT Models Predicting Alzheimer's Disease from Text

arXiv 2109.11888 ↗First posted 24 Sep 2021cs.CLcited by 5

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Abstract (arXiv)

Understanding robustness and sensitivity of BERT models predicting Alzheimer's disease from text is important for both developing better classification models and for understanding their capabilities and limitations. In this paper, we analyze how a controlled amount of desired and undesired text alterations impacts performance of BERT. We show that BERT is robust to natural linguistic variations in text. On the other hand, we show that BERT is not sensitive to removing clinically important information from text.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 22 of its 39 references that are in the map. First posted on arXiv 24 Sep 2021.

Builds on

22 of 39 references.

References · 22

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10 more
  1. 2020Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challengecited by 104
  2. 2018Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailmentcited by 59
  3. 2020To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detectioncited by 49
  4. 2018Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Modelscited by 26
  5. 2020Comparing Natural Language Processing Techniques for Alzheimer's Dementia Prediction in Spontaneous Speechcited by 14
  6. 2019Impact of ASR on Alzheimer's Disease Detection: All Errors are Equal, but Deletions are More Equal than Otherscited by 8
  7. 2018Detecting cognitive impairments by agreeing on interpretations of linguistic featurescited by 6
  8. 2018The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speechcited by 5
  9. 2020Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approachcited by 4
  10. 2019Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Powercited by 3

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 0; 2024: 2.

5 papers.

  1. 2022On the data requirements of probingcited by 4
  2. 2024A Systematic Review of NLP for Dementia -- Tasks, Datasets and Opportunitiescited by 4
  3. 2022DECK: Behavioral Tests to Improve Interpretability and Generalizability of BERT Models Detecting Depression from Textcited by 2
  4. 2022Impact of Environmental Noise on Alzheimer's Disease Detection from Speech: Should You Let a Baby Cry?cited by 1
  5. 2024Zero-Shot Multi-Lingual Speaker Verification in Clinical Trialscited by 0