Moses is a statistical machine translation system that allows you to automatically train translation models for any language pair. All you need is a collection of translated texts (parallel corpus). An efficient search algorithm finds quickly the highest probability translation among the exponential number of choices.
🙌🏼 Excited to share our work on Speech Foundation Model for data crowdsourcing at COLING 2025 🙌🏼
Our co-author Laurent Besacier (@laurent_besacie) at NAVER LABS Europe will be presenting -- don't miss it.
👉🏼 Details: https://mt.fbk.eu/1-paper-accepted-at-coling-2025
Exciting news: @iwslt is co-located with #ACL2025NLP again this year! 🎉
Interested in speech processing? Check out the new task on instruction following — any model can participate! 🚀
📅 Data release: April 1
⏳ Submission deadline: April 15
Don’t miss it! 💬 #NLP #SpeechTech
Weekly pick from the #MeetweenScientificWatch: “Video-SALMONN: Speech-enhanced audio-visual large language models” – Redefining video comprehension with speech-aware AV-LLMs and groundbreaking QA accuracy. 🎥🎤🤖
I’m glad to announce that our work “How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?” has been accepted at the Transactions of @aclanthology (TACL)! 🎉
The preprint is available here: