Language Models for Swedish Authorities

Purpose

RISE (Research Institutes of Sweden), the project coordinator, describes the project here as follows:

“This project provides the tools and prerequisites for Swedish authorities to build and integrate state-of-the-art NLP solutions in their current and future services. The development of state-of-the-art Swedish language models specifically designed for use in the public sector, and techniques for utilizing them, will enable novel types of NLP applications that have the potential to revolutionize the use of NLP in the Swedish public sector. The data, infrastructure and frameworks developed in the project will also enable the development of novel types of representation learning, and will thus ensure that Swedish NLP remains on the forefront of development."

The focus of the project is initially on four use cases: machine understanding and conversational AI, semantic textual similarity, entity tagging (named entity recognition) and text classification.

AI Sweden is responsible for coordinating and validating the data used in the project and for distributing the results and the knowledge to the reference group, external stakeholders and interested parties. Organising workshops and events (such as hackathons) will be part of the distribution activities, as well as to cooperate and share knowledge, experience, and results with the other NLP projects and data owners.

Background

The language models developed during the course of the project will eventually assist the authorities in sifting through, categorising and finding the right information in large amounts of text. By using NLP to make communication more automated, there is great benefit to society in significantly reducing costs and inefficient use of resources. Public authorities, for example, can be assisted with compiling and summarising reports, or cut normally long service queue times. When authorities need to manage large amounts of documentation, NLP will allow them to link together different text documents based on their content. For example, this benefits the Swedish Public Employment Service in improving the matching process between applicants and job adverts.