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Natural Language Generation Problems and Challenges

Talk by Prof. Konstantinos Diamantaras, Erasmus visiting professor, about Natural Language Generation Problems and Challenges


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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.

The event is hybrid: zoom link: Passcode: 551427