Publikation

Latent Feature Generation with Adversarial Learning for Aphasia Classification

Anna Vechkaeva, Günter Neumann

In: Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments. Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments (RaPID-2020) befindet sich LREC May 11-16 ISBN 979-10-95546-45-0 LREC 2020.

Abstrakt

Aphasia is a language disorder resulting from brain damage, and can be categorised into types according to the symptoms. Automatic aphasia classification would allow for quick preliminary assessment of the patients’ language disorder. A supervised approach to automatic aphasia classification would require substantial amount of training data, however, aphasia data is sparse. In this work, we attempt to use data generation, namely Generative Adversarial Networks (GANs), to deal with data sparsity. The latent feature generation approach is used to deal with the text generation non-differentiability problem, which is an issue for GANs. The approach using artificially generated data to augment training set was tested. We conclude through running a series of experiments that it has potential to improve aphasia classification in the context of low resource data, provided that the available data is enough for the generative model to properly learn the distribution.

Projekte

RaPID_3_paper_10.pdf (pdf, 228 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence