Language rehabilitation of people with BROCA aphasia using deep neural machine translation

Kamel Smaïli (1) , David Langlois (1) , Peter Pribil (1)

1 SMarT - Statistical Machine Translation and Speech Modelization and Text

More than 13 million people suffer a stroke each year. Aphasia is known as a language disorder usually caused by a stroke that damages a specific area of the brain that controls the expression and understanding of language. Aphasia is characterized by a disturbance of the linguistic code affecting encoding and/or decoding of the language. Our project aims to propose a method that helps a person suffering from aphasia to communicate better with those around him. For this, we will propose a machine translation capable of correcting aphasic errors and helping the patient to communicate more easily. To build such a system, we need a parallel corpus; to our knowledge, this corpus does not exist, especially for French. Therefore, the main challenge and the objective of this task is to build a parallel corpus composed of sentences with aphasic errors and their corresponding correction. We will show how we create a pseudo-aphasia corpus from real data, and then we will show the feasibility of our project to translate from aphasia data to natural language. The preliminary results show that the deep learning methods we used achieve correct translations corresponding to a BLEU of 38.6.

Mots clés: Aphasia; Augmentation corpus; Machine translation; Deep learning

Domaines: Informatique et langage [cs.CL]

Kamel Smaïli, David Langlois, Peter Pribil. Language rehabilitation of people with BROCA aphasia using deep neural machine translation. Fifth International Conference on Computational Linguistics in Bulgaria, Sep 2022, Sofia, Bulgaria. ⟨hal-03701615⟩