Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpus (N19-1)
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| Challenge: | Existing methods for automatic detection of Alzheimer's disease (AD) are limited by a lack of data. |
| Approach: | They propose a method to learn a correspondence between independently engineered lexicosyntactic features in two languages, using a large parallel corpus of out-of-domain movie dialogue data. |
| Outcome: | The proposed method outperforms both unilingual and machine translation-based baselines in Mandarin Chinese and is the first to transfer feature domains in detecting cognitive decline. |
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Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling (N19-1)
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Kathleen C. Fraser, Nicklas Linz, Bai Li, Kristina Lundholm Fors, Frank Rudzicz, Alexandra König, Jan Alexandersson, Philippe Robert, Dimitrios Kokkinakis
| Challenge: | Existing work on speech and language models has been limited by the size of available datasets. |
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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. |
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. |
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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. |
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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. |
| Outcome: | The proposed model achieves independent benchmark accuracy for the AD classification task. |
An LLM-based Temporal-spatial Data Generation and Fusion Approach for Early Detection of Late Onset Alzheimer’s Disease (LOAD) Stagings Especially in Chinese and English-speaking Populations (2025.findings-emnlp)
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| Challenge: | Existing approaches struggle with temporal-spatial challenges in capturing subtle linguistic shifts across different disease stages. |
| Approach: | They propose a large language model-driven T-S fusion framework that integrates multilingual LLMs, contrastive learning and interpretable marker discovery to revolutionize late onset AD detection. |
| Outcome: | The proposed framework achieves state-of-the-art performance in late onset AD detection while enabling cross-linguistic diagnostics. |
Tonal Salience in Cognitive Decline: In-Context MCI Detection with Multimodal LLMs (2026.acl-srw)
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| Challenge: | a recent study shows that tonal languages like Chinese have a higher classification performance than non-tonal languages like English. |
| Approach: | a new study examines the differences between tonal and non-tonal language classifications . they hypothesize that the difference is rooted in language typology . early cognitive decline is notoriously difficult to detect . |
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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. |
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LoSST-AD: A Longitudinal Corpus for Tracking Alzheimer’s Disease Related Changes in Spontaneous Speech (2024.lrec-main)
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| Challenge: | Language-based biomarkers have shown promising results in differentiating those with Alzheimer’s disease (AD) diagnosis from healthy individuals, but the earliest changes in language are thought to start years or even decades before the diagnosis. |
| Approach: | They propose to use transcripts of public interviews with 20 famous figures to track language change over several decades to validate their corpus. |
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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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