SAGOT Benoît

Most current research in NLP focuses on neural architectures that rely on large volumes of data, in the form of both raw text and costly annotated corpora. The increasing amount of data necessary to train such models is not available for all languages and can require massive computational resources. Moreover, these approaches are highly sensitive to language variation, illustrated for instance by domain-specific texts, historical documents and non-edited content as found on social media. To address these issues and allow for a wider deployment of NLP technologies, this bottleneck must be overcome. This will require new models that better exploit the complex structure of language and the context in which it is used.

SAGOT Benoît