NESPOLE! System has been developed using two scenarios: the tourism scenario and the first aid medical assistance scenario. During the project life three main data collection have been carried on in order to develop the first and the second showcase. During the first year 191 dialogues have been collected. There are 62 German dialogues recorded, 61 Italian, 37 English and 31 French. Particularly an amount of 6 hours of dialogues for Italian and French, 7 hours for English, 8 hours for German has been recorded. Dialogues were about five predefined tourism scenarios. During the last year two major data collections have been carried on: the first one aimed at expanding the tourism scenario and the second one at addressing the medical domain. For the monolingual data collection five tourism scenarios were developed; 66 dialogues were recorded yielding 994.57 minutes of data: 243.52 minutes comprised in sixteen English dialogues, 246 minutes in sixteen German dialogues, 272.52 minutes in seventeen French dialogues and 232.53 minutes in seventeen Italian dialogues. The data collection on the medical domain involved Italian, English and German languages. A total of 49 dialogues were collected. The recording results in a total of 8 hours 25 minutes of audio files.
Can algorithmic gender prediction ever be valid?
Check out this week's top pick by @lina_conti: "Algorithmic Gender Prediction Is Illegitimate, But Gender Imputation Can Yield Valid Measurements" by @evandongyx & @ang3linawang.
Pick of the week by @evandongyx & @ang3linawang:
https://arxiv.org/pdf/2608.13444
Predicting gender from images or names can reveal discrimination. But the practice itself harms trans people. This paper works through when that tradeoff might be justified and how to do it responsibly.
Our pick of the week by
@dhairya_su47605
: "Task-Circuit Quantization: Leveraging Knowledge Localization and Interpretability for Compression" by @hanqi_xiao, @yilin_sung, @EliasEskin and @mohitban47
#Quantization #Interpretibility
#PickoftheWeek @fbk_mt
Super cool paper on leavaraging Interpretability for Compression!
https://arxiv.org/pdf/2504.07389
Our pick of the week:
"Large Language Diffusion Model" by Shen Nie, Fengqi Zhu, @ZebinYou, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, @LiChongxuan
It is very cool to see how the researcher combine diffusion model and transformer blocks to train
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