Natural Language Processing
DESCRIPTION
Natural Language Processing has seen important advances over the past decade. While deep learning models have achieved impressive performance, even demonstrating common-sense knowledge, they are commonly viewed as uninterpretable black-boxes. We combine deep learning with deductive reasoning techniques from the symbolic Artificial Intelligence tradition, to produce new algorithms capable of reaching conclusions requiring multiple inference steps, using premises and conclusions expressed in natural language. We are also interested in understanding multilingual language models. While neural multilingual language models (MLMs) have notable success, their internal mechanics remain quite unclear. We explore symbolic approaches to understand the inner workings of large MLMs and investigate how to inject linguistic knowledge into neural models, aiming to learn how to benefit from human expertise and work with sparse data.
RESEARCHERS
Alexandros Potamianos
National Technical University of Athens
Angela Ralli
University of Patras
Antonios Anastasopoulos
George Mason University
Christos H. Papadimitriou
Columbia University
Dimitris Galanis
Athena Research Center
Grigorios Tsoumakas
Aristotle University of Thessaloniki
Ion (Ioannis) Androutsopoulos
Athens University of Economics and Business
John Pavlopoulos
Αthens University of Economics and Business
Katerina Pastra
Athena Research Center
Manolis Koubarakis
National and Kapodistrian University of Athens
Nikos Vasilakis
Brown University
Panagiotis Tsaparas
University of Ioannina
Pantelis John (PJ) Beaghton
Imperial College London
Sophia Ananiadou
University of Manchester
Stella Markantonatou
Athena Research Center
