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Dementia is associated with language disorders which impede communication.Here, we automatically learn linguistic disorder patterns by making use of amoderately-sized pre-trained language model and forcing it to focus onreformulated natural language processing (NLP) tasks and associated linguisticpatterns. Our experiments show that NLP tasks that encapsulate contextualinformation and enhance the gradient signal with linguistic patterns benefitperformance. We then use the probability estimates from the best model toconstruct digital linguistic markers measuring the overall quality incommunication and the intensity of a variety of language disorders. Weinvestigate how the digital markers characterize dementia speech from alongitudinal perspective. We find that our proposed communication marker isable to robustly and reliably characterize the language of people withdementia, outperforming existing linguistic approaches; and shows externalvalidity via significant correlation with clinical markers of behaviour.Finally, our proposed linguistic disorder markers provide useful insights intogradual language impairment associated with disease progression.

Reformulating NLP tasks to Capture Longitudinal Manifestation of Language Disorders in People with Dementia