Reformulating NLP tasks to Capture Longitudinal Manifestation of
  Language Disorders in People with Dementia
Dimitris Gkoumas Matthew Purver Maria Liakata
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.