Reformulating NLP tasks to Capture Longitudinal Manifestation of Language Disorders in People with Dementia. (2023)

Abstract: Dementia is associated with language disorders which impede communication. Here, we automatically learn linguistic disorder patterns by making use of a moderately-sized pre-trained language model and forcing it to focus on reformulated natural language processing (NLP) tasks and associated linguistic patterns. Our experiments show that NLP tasks that encapsulate contextual information and enhance the gradient signal with linguistic patterns benefit performance. We then use the probability estimates from the best model to construct digital linguistic markers measuring the overall quality in communication and the intensity of a variety of language disorders. We investigate how the digital markers characterize dementia speech from a longitudinal perspective. We find that our proposed communication marker is able to robustly and reliably characterize the language of people with dementia, outperforming existing linguistic approaches; and shows external validity via significant correlation with clinical markers of behaviour. Finally, our proposed linguistic disorder markers provide useful insights into gradual language impairment associated with disease progression.

## introduction dementia is a neuro-degenerative disease affecting millions worldwide and is associated with cognitive decline, including language impairment (forbes- @xcite . language dysfunction may be difficult to detect in the early stages of @xcite ; however, as the disease progresses, a gradual decline of semantic knowledge ensues, and eventually, all linguistic functions can be lost @xcite . recognizing language disorders as prodromal symptoms in people with dementia may help with earlier diagnosis and improve disease management. dementia can cause a variety of language deficits, such as: word-finding problems, a.k.a. early work in nlp for dementia relied on manual engineered features based on specific lexical, acoustic and syntactic features stemming from description tasks (such as ctp), to detect linguistic signs of cognitive decline @xcite @xcite . recent work uses naive neural approaches to classify and analyse linguistic and acoustic characteristics so as to either predict cognitive scores or achieve binary classification of participants (alzheimer's disease (ad) vs non-ad) @xcite @xcite . however, such approaches tend to learn language discrimination across cohorts ignoring explicit information entailed in linguistic ## conclusion we are the first to introduce reformulated nlp tasks for learning language disorder patterns from transcribed speech in dementia datasets by forcing a pre-trained language model to obtain signal from the language itself. our experiments show that nlp tasks encapsulating contextual information and enhancing the gradient signal with linguistic patterns benefit performance. we use the probability estimates of the model with highest macro-f1 to construct digital markers measuring communication ability and the occurrence of various language disorders in the speech of people with dementia and healthy controls. longitudinal analysis shows that the digital communication marker is able to assess the quality of communication and distinguish between people with mci, alzheimer's disease (ad) and healthy controls. a comparison against existing linguistic approaches for capturing language impairment shows the superiority of our proposed communication marker. moreover, the latter correlates significantly with two widely used clinical behaviour markers. finally, our proposed linguistic disorder markers prove effective for screening and monitoring ad and provide useful insights into longitudinal change in linguistic ability. in the future we will explore large pre-trained generative transformers and automatic generation of templates to improve performance on capturing linguistic disorder patterns.