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.
Our pick of the week by @FBKZhihangXie: "#Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in #SpeechLLMs" by @WangDingdo2603, Junan Li, @HelenMeng_CUHK, et al. (#EMNLP2025)
#SLU #SpeechTech
🚀 New paper: Speech Discrete Tokens or Continuous Features?
📄 https://aclanthology.org/2025.emnlp-main.1266.pdf
🧩 A comprehensive benchmark of SpeechLLMs using HuBERT/WavLM with Qwen & LLaMA.
✨ Continuous features outperform overall, while discrete tokens excel at phoneme-level detail.
🚀 Exciting news from the @FBK_MT group!
Four of our members @BeatriceSavoldi, @lina_conti, @negri_teo & @luisabentivogli are attending #EMNLP2025 in Suzhou 🇨🇳 with 5 accepted papers!
Come to our sessions & let's connect:
🔗 https://mt.fbk.eu/fbk-mt-at-emnlp-2025/
We’re also hiring postdocs!⚡
🎉🎓Congratulations to our PhD student @DennisFucci on a very successful thesis defense! 👏
Many thanks to the evaluation committee members @debora_nozza, @mirco_ravanelli, and Leonardo Badino for their insightful feedback and appreciation of his work!
#nlproc