Challenge: Existing work on speech and language models has been limited by the size of available datasets.
Approach: They propose to augment a small French dataset with a much larger English dataset to augment the language model to model the order in which information units are produced by dementia patients and controls.
Outcome: The proposed model improves classification performance in English and French separately.

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Towards Domain-Agnostic and Domain-Adaptive Dementia Detection from Spoken Language (2023.acl-long)

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Challenge: Domain adaptation (DA) techniques have been used to improve performance of NLP systems for healthcare tasks due to numerous complexities of data.
Approach: They propose to use domain adaptation techniques to improve generalizability across diverse datasets for dementia detection.
Outcome: The proposed model achieves a 22% increase in accuracy adapting from a conversational to task-oriented dataset compared to a jointly trained baseline.
Enriching Neural Models with Targeted Features for Dementia Detection (P19-2)

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Challenge: In the United States, adults over 65 are expected to comprise one-fifth of the population by 2030, and a larger proportion of the . population than those under 18 by 2035.
Approach: They propose a neural model that takes into account both long language samples and hand-crafted linguistic features to distinguish between dementia affected and healthy patients.
Outcome: The proposed model achieves an F1 score of 0.929 on the DementiaBank dataset and the state-of-the-art on the dataset.
Dementia Through Different Eyes: Explainable Modeling of Human and LLM Perceptions for Early Awareness (2025.findings-emnlp)

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Challenge: a new study examines how dementia is perceived by non-experts . human perception of dementia is inconsistent and relies on a narrow set of cues compared to LLMs based on broader clinical patterns .
Approach: They propose a method that uses LLMs to extract high-level, expert-guided features . human perception of dementia is inconsistent and relies on a narrow set of cues, they say .
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Detecting Linguistic Characteristics of Alzheimer’s Dementia by Interpreting Neural Models (N18-2)

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Challenge: Current diagnoses often involve lengthy medical evaluations.
Approach: They apply neural models based on CNNs, LSTM-RNNs, and their combination to classify AD and control language samples.
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Adversarial Text Generation using Large Language Models for Dementia Detection (2024.emnlp-main)

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Challenge: Large language models excel in text classification tasks, but they do not perform well with picture description.
Approach: They propose an interpretable classification approach by Adversarial Text Generation (ATG) that could relate dementia detection with other tasks.
Outcome: The proposed approach achieves 85% accuracy, >10% improvement over the previous methods.
Augmenting word2vec with latent Dirichlet allocation within a clinical application (N19-1)

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Challenge: Existing models that combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer’s disease from transcripts of picture descriptions are not suitable for clinical binary text classification tasks.
Approach: They propose three models that combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer’s disease from transcripts of picture descriptions.
Outcome: The proposed models outperform word2vec and LDA models on a clinical binary text classification task.
Domain Adaptation via Prompt Learning for Alzheimer’s Detection (2024.findings-emnlp)

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Challenge: Existing attempts to fine-tune pre-trained language models for AD detection are limited by the small and disparate corpora of spoken language.
Approach: They propose to use domain-adaptive prompt fine-tuning to optimize for AD detection by using AD classification loss as the training objective and spoken language corpora from a variety of language tasks.
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The Trade-offs of Domain Adaptation for Neural Language Models (2022.acl-long)

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Challenge: Neural Language Models (LMs) trained on large generic training sets have been shown to be effective at adapting to smaller, specific target domains for language modeling and other downstream tasks.
Approach: They propose a framework for a Neural Language Models (LM) to be presented in a common framework.
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Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection (2020.coling-main)

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Challenge: Existing methods for AD detection are too expensive and time-consuming to cover all potential patients.
Approach: They propose a contrastive learning method to obtain effective text representations based on monolingual embeddings of BERT and a cross-lingual data augmentation method by building autoencoders to learn the text representation shared by both languages.
Outcome: The proposed method outperforms other methods on a Mandarin AD corpus and achieves 81.6% detection accuracy.
A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer’s Type (2020.acl-main)

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Challenge: Recent studies show that cognitive manifestations of future dementia may appear as early as 18 years prior to clinical diagnosis . lack of clear diagnosis and prognosis, possibly for an Alzheimer's type, is a major limitation of current methods for identifying dementia-specific cognitive markers.
Approach: They propose to interrogate neural LMs trained on participants with and without dementia by manipulating lexical frequency.
Outcome: The proposed model improves upon the current state-of-the-art for models trained on transcripts of speech produced by healthy participants and those with dementia.

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