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.
π Call for Participation: @iwslt Offline Speech Translation 2026
Break language barriers with new languages & real-world scenarios + a brand new source-language agnostic speech translation track π
π
Evaluation: Apr 1β15
π
#IWSLT2026 #SpeechAI
π Call for Participation: @iwslt Model Compression 2026
Make large multilingual foundation models small β‘ without losing power in ENβDE/ZH speech-to-text translation.
π
Evaluation: Apr 1β15
#IWSLT2026 #SpeechAI #Qwen2 #EfficientAI
π Call for Participation: @iwslt Subtitling 2026
Turn speech into ready-to-watch subtitles π¬ across TV, News & YouTube!
π
Evaluation: Apr 1β15
#IWSLT2026 #SpeechAI #MultimodalAI