Natural Language Processing
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.
- Collective Intelligence Centre, Univ. Technology, Sydney releases open source Academic Writing Analytics infrastructure collaborating with Ágnes Sándor and Claude Roux.
- Ágnes Sándor appointed honorary associate at the University of Technology, Sydney
- NAACL oustanding reviewer: Marc Dymetman
- SMOOTH GDPR H2020 project accepted and kicked off in May
- The team is giving an NLP course at Centrale Supelec
- In collaboration with our colleagues from CV, we ranked first at the MediaEval 2017 Retrieving Diverse Social Images Task challenge.
- AI/data-mining papers: full papers at UAI, RO-MAN (conversational agents), IJCAI (Q&A), KDD (real-time bidding) and AISTATS (spectral methods).
- Best Full Paper at the 7th International Learning Analytics and Knowledge Conference (LAK'17), 'Reflective Writing Analytics for Actionable Feedback'
- 3 papers and a demo at EACL in Valencia
- Why sticking a head on a robot doesn’t make it any more social
- AI and democracy – AI could make everyone’s voice heard