Department of Software and Computing Systems


Title:Natural Language Generation Problems and Challenges Import to your calendar:
Conferència convidada
Presenter:Prof. Konstantinos Diamantaras (International Hellenic University, Greece)
Venue:Sala Ada Lovelace
Date&time:12:00 26/04/2023
Estimated duration:1:30 horas
More information:
Contact person:

Lloret Pastor, Elena (elloret[Perdone'm]
Abstract: Natural Language Generation problems like question-answering,
text summarisation, and machine translation are nowadays tackled using
mainly transformer-based machine learning models such as GPT-3, T5, and
BART. Although these sophisticated models achieve very good performance in
most NLG tasks, they suffer from a fundamental lack of common sense. For
this reason, they often generate implausible and “strange” sentences or
sentences that are short and simple, avoiding the rich and natural structures
generated by humans. Recently there is an increasing trend to incorporate
common sense reasoning in text generation. The aim is to enhance/enrich
the process of natural language generation using external knowledge, which
exists in many data sources like Wikipedia, knowledge bases, and knowledge
graphs. Common-sense knowledge is one example of knowledge that can be
acquired from knowledge bases/graphs and can be used in the generation process
by creating embeddings. The focus of this talk is to present methods that
incorporate external knowledge available in various common-sense knowledge
bases into state-of-the-art natural language generation models.

Konstantinos Diamantaras received the Diploma degree from the National
Technical University of Athens, Greece, in 1987 and the Ph.D. degree in
Electrical Engineering from Princeton University, Princeton, NJ, USA,
in 1992. He joined the Department of Information Technology, in the TEI of
Thessaloniki, Greece as a faculty member in 1998. He is currently a Professor
at the Department of Information and Electronics Engineering, International
Hellenic University, Greece. His research interests include machine learning,
signal / image processing and parallel computing. Dr. Diamantaras has served
as the Chairman to the Machine Learning for Signal Processing (MLSP) Technical
Committee (TC) of the IEEE Signal Processing Society and a member of the MLSP
and Signal Processing Theory and Methods TCs as well. He has been the Chairman
and a member of the TC for various machine learning, signal processing, and
neural networks conferences. He has served as an Associate Editor for the
IEEE Transactions on Signal Processing, the IEEE Signal Processing Letters,
and the IEEE Transactions on Neural Networks.
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