The Machine Translation group at Fondazione Bruno Kessler specializes in speech and text technologies for multilingual communication. We work on innovative neural architectures and multimodal resources for a range of topics and applications (e.g. subtitling, interpreting, gender inclusivity). We are part of the Digital Industry center.
If you are fascinated by languages, cutting edge technology and research challenges, check out our Join Us page!
LT Highlights @ FBK 2026: Le novità della ricerca sulle tecnologie del linguaggio
Nov 10, 2026
The Language Technology Research Highlights 2026 event aims to bring together scientists,...
-
-
MT Unit at EMNLP 2026!
Oct 23, 2026
-
Luisa Bentivogli at Interspeech2026 in Sydney 🇦🇺
Oct 13, 2026
-
3 Papers Accepted at CLiC-it 2026 2026
Aug 5, 2026
-
Our #PickOfTheWeek 📚: “Training Dynamics Impact Post-Training Quantization Robustness” by @actatjer, @n_ajroldi, @jonasgeiping
Pick of the week @fbk_mt
Cool paper on how robustness to quantization in LLMs is also impacted by training conditions, instead of just the number of training tokens!
https://arxiv.org/abs/2510.06213
Our #PickOfTheWeek 📚: “High-Fidelity Simultaneous Speech-To-Speech Translation” by @t0m1ab, @lmazare, @EXGRV, @ptrkprz, @honualx and @neilzegh
🎙️➡️🌍 This work shows how a full-duplex SpeechLLM architecture can be adapted for simultaneous S2ST and S2TT.
Can LLM Agents Learn how to behave According to your preferences?
PersonaAgent is a framework that combines personalized memory and tool use to help LLM agents adapt their actions to individual users without fine-tuning the underlying model.
Our #PickOfTheWeek, selected by @mgaido91: "Alignment Is All You Need: Instruction-Free Training for General Audio-Language Models" by Xuanru Zhou, Yiwen Shao, Jiahong Li, and Dong Yu 🎧
Can instruction-free training power the next generation of audio-language models?
Cool to see we are not the only ones working on building SpeechLLMs with instruction-following capabilities without requiring LLM adaptation and instruction-following data that is expensive to collect!
https://arxiv.org/abs/2608.18132
@fbk_mt