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.
🚀 New tech report out! Meet FAMA, our open-science speech foundation model family for both ASR and ST in 🇬🇧 English and 🇮🇹 Italian.
The models are live and ready to try on @huggingface 👇
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#ASR #ST #OpenScience #MultilingualAI
Our pick of the week by @lina_conti: "Languages in Multilingual Speech Foundation Models Align Both Phonetically and Semantically" by @soheunshim, Domenico De Cristofaro, Chengzhi Martin Hu, Alessandro Vietti, and @barbara_plank (2025).
#speech #SFM #multilingual #speechtech
Pick of the week @fbk_mt: https://arxiv.org/abs/2505.19606 by @soheunshim @DomenicoDeCris1. XAI work on cross-lingual alignment in speech-to-text models that disentangles phonetics and semantics. Plus: their XAI insights yield actionable improvements for low-resource language performance.
🚀 New shared task at #WMT2025 (co-located with @emnlpmeeting ): Model Compression for Machine Translation!
Can you shrink an LLM and keep translation quality high?🔧
Submit by July 3 and push the limits of efficient NLP!
👉 https://www2.statmt.org/wmt25/model-compression.html #NLP #ML #LLM #ModelCompression
More great news! 🎉
Our paper “Echoes of Phonetics: Unveiling Relevant Acoustic Cues for ASR via Feature Attribution” was accepted at #Interspeech2025!
Interested in interpretability for speech models? Preprint coming soon!
✍🏼 @mgaido91, @negri_teo, M.Cettolo, @luisabentivogli