The scientific and technological objectives of QALL-ME pursued three crucial directions: multilingual open domain QA, user-driven and context-aware QA, and learning technologies for QA. The specific research objectives of the project included state-of-art advancements in the complexity of the questions handled by the system(e.g. how questions); the development of a web-based architecture for cross-language QA (i.e. question in one language, answer in a different language); the realization of real-time QA systems for concrete applications; the integration of the temporal and spatial context both for question interpretation and for answer extraction; the development of a robust framework for applying minimally supervised machine learning algorithms to QA tasks; and the integration of mature technologies for automatic speech recognition within the open domain question answering framework.
Our pick of the week by @lina97337786: "Towards a Deep Understanding of Multilingual End-to-End Speech Translation" by @hrsun42, @YikunLei et al., Findings EMNLP 2023.
#NLP #NLProc #EMNLP2023 #speech #translation #multilingual
🎉 AI-GAP workshop wrapped up yesterday!
Huge thanks to the participants, speakers, and organizers -- we were very happy, as you can see :)
@donatellado @BeatriceSavoldi @costanzalfieri @ECappu Clara Punzi & Laura State
🎊 I'm very happy to share that the paper "Direct Speech Translation for Automatic Subtitling" accepted by the #TACL journal is now published and available at:
#NLP #NLProc #subtitling #speech #translation
📢 @BeatriceSavoldi from @fbk_mt (@FBK_research) is co-organizing the AI GAP Workshop, held in collaboration with @Unipisa, @univaq, and @scuolanormale!
👉 Check the website for more information:
#AI #ArtificialIntelligence #AlgorithmicBias