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
@BeatriceSavoldi
: "Accuracy: Community Perspectives on Machine Translation" by Yujun Wang,
@EhudReiter
, Shimei Pan,
@egere14
and Wei Zhao #MachineTranslation #TranslationQuality #Evaluation
📖 #PickoftheWeek @fbk_mt "Accuracy: Community Perspectives on Machine Translation"
A cool analysis of the conflicting interests of different communities around MT(AI developers, LSPs, and users)
https://arxiv.org/pdf/2606.09655
#NLP #MachineTranslation #DiverseStakeholders
We are at the Alliance for Language Technologies - #ALTEDIC Week 2026!
@luisabentivogli and @negri_teo are attending two project meetings (ALT-EDIC4EU and #LLMs4EU), presenting the Evaluation of Spoken Language Translation in the context of IWSLT.
#LanguageTechnologies #FBK
3️⃣ "Cross-Attention is Half Explanation in Speech-to-Text Models"
👥 @sarapapi, @DennisFucci, @mgaido91, @negri_teo, @luisabentivogli
🇪🇺 DVPS EU project
📄