Data-intensive NLP and deep neural NLP models

Deep neural language and translation models have revolutionized modern natural language processing. Data-intensive approaches using deep learning and artificial neural networks are applied in the majority of NLP tasks and require extensive high-performance computing.

Projects:

• HPLT – High-Performance Language Technologies (2022-2025)
• Uncertainty-aware neural language models (2022-2024)
• Behind the words: Deep neural models of language meaning for industry-grade appliciations (2021-2023)
• FoTran – Found in Translation (ERC), (2018-2024)
• OPUS-MT – Open Translation Models, Tools and Services (ELG pilot pro)
• EOSC-nordic – the NLPL use case inside the nordic chapter of the European Open Science Cloud
• OPUS (supported by NLPL), including OPUS-MT

People:

• Jörg Tiedemann
• Mathias Creutz
• Hande Celikkanat
• Michele Boggia
• Stig-Arne Grönroos
• Timothee Mickus
• Marianna Apidianaki
• Sami Virpioja
• Khalid Alnajjar
• Juan Raul Vazquez Carrillo
• Aarne Talman
• Mikko Aulamo
• Eetu Sjöblom
• Robert Östling (2016)

Publications:

• Publications from the FoTran project
• OPUS-related publications