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: "When End-to-End is Overkill: Rethinking Cascaded Speech-to-Text Translation" by Anna Min, et al, 2025.
Today's task: model compression!!
🎯 Goal: Compress a large, general-purpose multimodal model, making speech translation more efficient ⚡️, deployable 📲, and sustainable ♻️, while preserving translation quality ⭐️
#AI #SpeechTech #ModelCompression #LLMcompression
First up, a new task for 2025:
*Instruction-following for speech processing!*
Explore instruction-following for speech ⇨
Integrate speech foundation models with LLMs across tasks such as speech translation, recognition, summarization, and QA.
🔗:
📢Workshop gratuito 05/02: “Lo stato dell'arte nelle tecnologie per il riconoscimento del parlato.”
Diretta YouTube: https://www.youtube.com/live/i4x7w8fIIXo?si=wYvvrO3-MSh7Yik4
Registrazione: https://www.eventbrite.com/e/biglietti-lo-stato-dellarte-nelle-tecnologie-per-il-riconoscimento-del-parlato-1109098797359?aff=oddtdtcreator