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 @mgaido91: "AlignFormer: Modality Matching Can Achieve Better Zero-shot Instruction-Following Speech-LLM" by @RuchaoFan, Bo Ren, Yuxuan Hu, Rui Zhao, Shujie Liu, Jinyu Li (2024).
#NLProc #Speech #instructionfollowing #zeroshot #speechtech #speechllm
AI is transforming cultural heritage, but what have we learned?
Come and join the #AI4Culture movement at our Final Conference on March 10 in Hilversum to explore AI’s current & future impact on cultural heritage.
Details & Registration: https://pretix.eu/EFHA/AI4Culture/
@EU_HaDEA
BOUQuET💐: an OPEN INITIATIVE aimed at building an evaluation dataset for massively multilingual text-to-text MT.
Let’s make MT available for any written language!
We are inviting everyone to contribute: ➡️
More details at: https://arxiv.org/abs/2502.04314
I am happy to announce that I will speak about our recent work "How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?" at the SlatorCon in March 🎊
📃 Preprint available here: