JUMAS addresses the need to build an infrastructure able to optimise the information workflow in order to facilitate later analysis. New models and techniques for representing and automatically extracting the embedded semantics derived from multiple data sources will be developed. The most important goal of the JUMAS system is to collect, enrich and share multimedia documents annotated with embedded semantic minimising manual transcription activity. JUMAS is tailored at managing situations in which multiple cameras and audio sources are used to record assemblies in which people debates and event sequences need to be semantically reconstructed for future consultations. The prototype of JUMAS will be tested interworking with legacy systems, but the system can be viewed as able to support business processes and problem-solving in a variety of domains.
SHADES: a global dataset to uncover AI bias
Over 50 researchers, 16 languages, thousands of interactions analysed: the international SHADES project investigates how generative language models (LLM) reproduce and amplify cultural stereotypes
◾https://magazine.fbk.eu/en/news/shades-the-new-global-dataset-to-monitor-as-ai-reproduces-and-invents-cultural-stereotypes/
🎉 Excited to share our paper “Different Speech Translation Models Encode and Translate Speaker Gender Differently” was accepted at #ACL2025 (main)!
✍🏼 Big thanks to amazing co-authors: @mgaido91, @negri_teo, @luisabentivogli, @andre_t_martins, @peppeatta!
📄 Preprint out soon!
🎉 Excited to share that our @sarapapi has won the 2024 Best PhD Award from the Information and Engineering Doctoral School at @UniTrento_DISI for her thesis “Direct Speech Translation in Constrained Contexts: The Simultaneous and Subtitling Scenarios.”
#nlproc @FBK_research
🎉 Excited to share that our @sarapapi has won the 2024 Best PhD Award from the Information and Engineering Doctoral School at @UniTrento_DISI for her thesis “Direct Speech Translation in Constrained Contexts: The Simultaneous and Subtitling Scenarios.”
#nlproc @FBK_research