University of Helsinki
Deep neural models of language meaning for industry-grade applications
In this project, we propose the development of a unique data resource, models, and tools for improved neural meaning representations that are robust to non-trivial rephrasing of statements with equivalent, or near-equivalent meaning. Such models and tools can significantly boost the coverage and precision in industrial language content management, data mining and internal as well as external communication procedures. The project will deliver a dedicated data collection and research focused on paraphrase encoding and generation that will pave the way to significantly improved language models operating on more appropriate semantic abstractions instead of relying on surface-oriented, lexical or syntactic overlaps or spurious relations that can be learned from purely collocational distributions. The focus is on Finnish and Swedish.
- 2021-2023, funded by the Academy of Finland
- Consortium: TurkuNLP and HelsinkiNLP
Research team in Helsinki
- Jörg Tiedemann (Administrative PI)
- Mathias Creutz (Scientific PI)
- Eetu Sjöblom (Research assistant)
- Teemu Vahtola (Doctoral researcher)
- Sami Itkonen (Doctoral researcher)
