Invited speakers

 Marko Grobelnik

 Biography

Marko Grobelnik is an expert researcher in the field of Artificial Intelligence (AI). Focused areas of expertise are Machine Learning, Data/Text/Web Mining, Network  Analysis, Semantic Technologies, Deep Text Understanding, and Data Visualization. Marko co-leads the Department for Artificial Intelligence at Jozef Stefan Institute,  co-founded UNESCO International Research Center on AI (IRCAI), and is the CEO of Quintelligence.com specialized in solving complex AI tasks for the  commercial world.

 He collaborates with major European academic institutions and major industries such as Bloomberg, British Telecom, European Commission, Microsoft Research,   New York Times. Marko is co-author of several books, co-founder of several start-ups and is/was involved into over 50 EU funded research projects in various fields of Artificial Intelligence. Marko represents Slovenia in OECD AI Committee (AIONE) and in Council of Europe Committee on AI (CAHAI). In 2016 Marko became Digital Champion of Slovenia at European Commission.

Magnus Sahlgren

GPT-SW3: building the first large generative language model for Swedish

Abstract

This talk gives an overview over the process of building the first large generative language model for Swedish. We cover the motivation for building the model, as well as challenges and opportunities with data and compute. We also give examples of applications of the model, and discuss future directions for building and deploying large language models for smaller languages.

Biography

Magnus Sahlgren, PhD, is Head of Research for Natural Language Understanding at AI Sweden. Sahlgren's research lies at the intersection between computational linguistics, philosophy, and artificial intelligence. He is primarily known for his work on computational models of meaning.

 ​Jan Hajič

Multilingual Language Technology in the age of Artificial Intelligence and Deep Neural Networks

Abstract

Language Technology made a lot of progress since the first systems developed more than 50 years ago, and even bigger progress after the first (statistical) machine learning has been applied to it in the early 1990s. Recently, Artificial Neural Networks took over methodologically and technically in all areas of LT, providing much more quality in many LT applications, sometimes on par or better than the performance of humans on these tasks, including Machine Translation. While many of these technologies are now available to almost everybody thanks to the large multinational companies, education, research and innovation needs open access to language resources, basic language technology tools and services to try and apply new ideas. In the talk, the role of research infrastructures and international networks and platforms in supplying these data, tools and services (such as CLARIN) as well as the goals recent European projects, like the High Performance Language Technology project building open large language and translation models, will be presented from this perspective.

Biography

Jan Hajič is a full professor of Computational Linguistics at the Institute of Formal and Applied Linguistics at the School of Computer Science, Charles University, Prague, Czechia. His interests cover morphology and part-of-speech tagging of inflective languages, machine translation, deep language understanding, and the application of statistical methods in natural language processing in general. He also has an extensive experience in building language resources for multiple languages with rich linguistic annotation, and is currently the director of a large, multi-institutional research infrastructure on language resources in the Czech Republic, LINDAT/CLARIAH-CZ, which aims at making datasets and corpora openly available for linguistic and Digital Humanities research. His work experience includes both industrial research (IBM Research Yorktown Heights, NY, USA, in 1991-1993) and academia (Charles University in Prague, Czech Republic and Johns Hopkins University, Baltimore, MD, USA, 1999-2000, and adjunct position at University of Colorado, USA, 2017-2022). He has published more than 200 conference and journal papers, a book on computational morphology, and several other book chapters, encyclopedia and handbook entries. He regularly teaches basic and advanced courses on Statistical NLP and has multiple experience giving tutorials and lectures at various international training schools. He has been the PI or Co-PI of numerous international as well as large national grants and projects (including EU Framework Programme projects, such as H2020, HE, and the NSF ITR program in the U.S.). He is the chair of the Executive Board of META-NET, European research network in Language Technology.