Natural Language Processing image

Language is the most natural and dominant mode of communication, and arguably one of the main visible signals of higher intelligence. At the same time, language is messy, ambiguous and ever-changing so to decipher it you need a good amount of cultural, common-sense and contextual understanding. To fulfill our vision of Ambient Intelligence where intelligent devices communicate seamlessly with us, we need to considerably improve existing technology and methods that solve NLP problems. That’s exactly what we do.

We address what's often called the “Natural Language Understanding” part, by going beyond simple named entity extraction to get the real meaning of user-generated-content, both the objective part as well as the subjective one. We match our understanding of the textual item to our understanding of the needs of the human to provide the right textual item at the right time. We work on the generation of natural language, to produce text that is simple and natural to understand, but still accurate and grammatically correct.

As a European lab of a Korean company we’re distinctly aware of how real the language barrier can be, and we improve the current state-of-the-art in multilingual applications and machine translation.

Method-wise, we’re particularly interested in how to combine the power and flexibility of deep neural networks with the rich prior knowledge present in decades of linguistic studies and prior knowledge of the task at hand. This gives us better results with less training data.

Sensitive to the tension between our data-hungry algorithms and the importance of protecting privacy we develop privacy-preserving data-mining techniques.



  • SMOOTH GDPR H2020 project accepted [DG CNECT]
  • The team is giving a series of NLP classes at Centre Scientifique Orsay
  • Marc Dymetman amongst Outstanding Reviewers of NAACL HLT 2018


Blog articles

[MAFIA MEETUP#1] Conversational robots by Matthias Gallé