Machine Learning and 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

angela-ralli
Angela Ralli
University of Patras
antonios-anastasopoulos
Antonios Anastasopoulos
GEORGE MASON UNIVERSITY
christos-h-papadimitriou
Christos H. Papadimitriou
Columbia University
dimitris-galanis
Dimitris Galanis
Research Center 'Athena'
ion-ioannis-androutsopoulos
Ion (Ioannis) Androutsopoulos
Athens University of Economics and Business
stella-markantonatou
Stella Markantonatou
Research Center Athena
 

The project “ARCHIMEDES Unit: Research in Artificial Intelligence, Data Science and Algorithms” with code OPS 5154714 is implemented by the National Recovery and Resilience Plan “Greece 2.0” and is funded by the European Union – NextGenerationEU.

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