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
@mgaido91
: "FlexiSLM: A Dynamic and Controllable Frame Rate Spoken Language Model" by Jiaqi Li, Chaoren Wang, Xiaohai Tian, Mingjie Chen, Xinyu Liang, Xu Li, Yufan Lin, Junwen Qiu, Jun Zhang, Lu Lu, Haizhou Li and @drwuz
#SLM #EfficientInference
Cool to see a work that adaptively chooses at inference how much to compress the input speech sequence, to control inference costs and quality based on the input, without enforcing a global trade-off to each segment: https://arxiv.org/pdf/2606.31247
@fbk_mt
Our pick of the week by
@FBKZhihangXie : "Speech-XL: Towards Long-Form Speech Understanding in Large Speech Language Models" by Haoqin Sun, @Chenyang_Lyu, Shiwan Zhao, Xuanfan Ni, Xiangyu Kong, @wangly0229, Weihua Luo and Yong Qin
#SpeechLLM #LongFormSpeech #SLU
🚀 New paper: Speech-XL for long-form SpeechLLMs
📄 https://arxiv.org/abs/2602.05373
🧩 Uses Speech Summarization Tokens to compress local speech intervals into compact KV states efficiently.
✨ Improves long-form speech understanding while reducing memory and FLOPs on 10-minute audio.
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