The data statement of the MuST-SHE Corpus (V1.2) provides information that is relevant to judge the features and content of the corpus. It covers how the corpus was curated, describes the gender phenomena involved, and provides demographic information for annotators.
The data statement was created within the Data Statements Project at the University of Washington.
You can download the Data Statement HERE
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
