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 @DennisFucci: "Encoding of lexical tone in self-supervised models of spoken language" by @linguisticshen, @Phonologician
@afraalishahi, @AriannaBisazza, and @gchrupala, NAACL 2024.
#Speech #SpokenLanguageModels #ToneEncoding #Interpretability #Phonology
⏳Hurry up! Only 4 days left to apply for a PhD position on “Resource-efficient Foundation Models for Automatic Translation” (A10). Don't miss this opportunity!
📅 Deadline: May 7, 4pm (CEST)
👉Info: https://iecs.unitn.it/education/admission/call-for-application
#PhD #NLProc
Last on our power panel: none other than @HelenaMoniz5 🤩 President of @EAMTee and the International Association of Machine Translation. Currently Chair of the Ethics Committee of the Center for Responsible AI (https://centerforresponsible.ai/) with @Unbabel You don't want to miss this!
Our pick of the week by @beomseok_lee_: "UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions" by @Sid_Arora_18, @emonosuke, @pengyf21, @RoshanSSharma2, @shinjiw_at_cmu, et al., 2024.
#SLU #languageunderstanding #speech