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.
Late update, but we had two great talks last month!
#MachineTranslation #FBK #NLProc #GenderBias #SpeechSynthesis
Our pick of the week by @dhairya_su47605
: "Scaling Laws for Precision" by @tanishqkumar07, Zachary Ankner, @bfspectorShiekh, @blake__bordelon, @Muennighoff, @mansiege, @CPehlevan, Christopher R´e, @AdtRaghunathan
📰
#Quantization #LLM #ScalingLaw
Pick of the week @fbk_mt
Super interesting paper on the limitations of quantization, demonstrating how post-training quantization scales poorly in data.
https://arxiv.org/abs/2411.04330
⭐ For our #PickOfTheWeek, this paper explores an important question for modern speech AI:
🎙️ Which Evaluation for Which Speech Model?
👥 Authors: @Maureendss , @EeshanDhekane
Speech foundation models are evolving rapidly, but evaluation practices are still fragmented.
🏝️ Yesterday at #LREC2026, Palma de Mallorca!
@lina_conti presented "Voice, Bias, and Coreference: An Interpretability Study of Gender in Speech Translation" at the poster session.
📄Paper:
💻Code: https://github.com/lina-conti/voice-bias-coreference
#SpeechTranslation #NLProc