EMBEDDIA project: Cross-Lingual Embeddings for Less- Represented Languages in E uropean News Media (2022)

Abstract: EMBEDDIA project developed a range of resources and methods for less-resourced EU languages, focusing on applications for media industry, including keyword extraction, comment moderation and article generation.

## introduction in the eu, websites and online services for citizens offer resources in national local languages, and often only provide a second language (usually english) when absolutely needed. for the eu to realise a truly equitable, open, multilingual online content and tools to support its management, new multilingual technologies which do not rely on translation of text between languages are urgently needed. the aim of embeddia was to address these challenges by leveraging innovations in the use of cross-lingual and multilingual embeddings coupled with deep neural networks to allow existing monolingual resources to be used across languages, leveraging their high speed of operation for near real-time applications, without the need for large computational resources. across more than three years (01/01/2019 to 31/12/2021), six academic partners (jozef stefan institute, the coordinating partner, queen mary university of london, university of ljubljana, university of la rochelle, university of helsinki and university of edinburgh) and four industry partners (texta o ü, as ekspress meedia, finnish news agency stt and trikoder d.o.o.) developed novel solutions with focus on less-represented eu languages, and tested them in real-world media production contexts. the main scientific goals of the project were to: ## tools the main embeddia tools are made available for future use through the embeddia media assistant, available at https://embeddia . texta.ee/ consisting of: