MateCat worked at a new frontier of Computer Assisted Translation (CAT) technology, that is, how to effectively and ergonomically integrate Machine Translation (MT) within the human translation workflow. At a time in which MT was mainly trained with the objective of creating the most comprehensible output, MateCat targeted the development of MT technology aimed at minimizing the translator’s post-edit effort. To this end, MateCat developed an enhanced web-based CAT tool offering new MT capabilities, such as automatic adaption to the translated content, online learning from user corrections, and automatic quality estimation.
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