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Your search keyword '"Giovanni Da San Martino"' showing total 20 results
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20 results on '"Giovanni Da San Martino"'

4. Overview of the CLEF-2024 CheckThat! Lab Task 3 on Persuasion Techniques.

Author

Jakub Piskorski, Nicolas Stefanovitch, Firoj Alam, Ricardo Campos 0001, Dimitar Dimitrov, Alípio Jorge, Senja Pollak, Nikolay Ribin, Zoran Fijavz, Maram Hasanain, Purificação Silvano, Elisa Sartori, Nuno Guimarães, Ana Zwitter Vitez, Ana Filipa Pacheco, Ivan Koychev, Nana Yu, Preslav Nakov, and Giovanni Da San Martino

Published
2024

16. SemEval-2024 Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes

Author

Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá, Mickus, T, Zosa, E, Vazquez, R, Vahtola, T, Tiedemann, J, Segonne, V, Raganato, A, Apidianaki, M, Mickus, Timothee, Zosa, Elaine, Vazquez, Raul, Vahtola, Teemu, Tiedemann, Jörg, Segonne, Vincent, Raganato, Alessandro, Apidianaki, Marianna, Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá, Mickus, T, Zosa, E, Vazquez, R, Vahtola, T, Tiedemann, J, Segonne, V, Raganato, A, Apidianaki, M, Mickus, Timothee, Zosa, Elaine, Vazquez, Raul, Vahtola, Teemu, Tiedemann, Jörg, Segonne, Vincent, Raganato, Alessandro, and Apidianaki, Marianna

Abstract

This paper presents the results of the SHROOM, a shared task focused on detecting hallucinations: outputs from natural language generation (NLG) systems that are fluent, yet inaccurate. Such cases of overgeneration put in jeopardy many NLG applications, where correctness is often mission-critical. The shared task was conducted with a newly constructed dataset of 4000 model outputs labeled by 5 annotators each, spanning 3 NLP tasks: machine translation, paraphrase generation and definition modeling.The shared task was tackled by a total of 58 different users grouped in 42 teams, out of which 26 elected to write a system description paper; collectively, they submitted over 300 prediction sets on both tracks of the shared task. We observe a number of key trends in how this approach was tackled—many participants rely on a handful of model, and often rely either on synthetic data for fine-tuning or zero-shot prompting strategies. While a majority of the teams did outperform our proposed baseline system, the performances of top-scoring systems are still consistent with a random handling of the more challenging items.

Published
2024

17. Accepted Tutorials at The Web Conference 2022

Author

Riccardo Tommasini, Senjuti Basu Roy, Xuan Wang, Hongwei Wang, Heng Ji, Jiawei Han, Preslav Nakov, Giovanni Da San Martino, Firoj Alam, Markus Schedl, Elisabeth Lex, Akash Bharadwaj, Graham Cormode, Milan Dojchinovski, Jan Forberg, Johannes Frey, Pieter Bonte, Marco Balduini, Matteo Belcao, Emanuele Della Valle, Junliang Yu, Hongzhi Yin, Tong Chen, Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Jamell Dacon, Lingjuan Lye, Jiliang Tang, Aristides Gionis, Stefan Neumann, Bruno Ordozgoiti, Simon Razniewski, Hiba Arnaout, Shrestha Ghosh, Fabian Suchanek, Lingfei Wu, Yu Chen, Yunyao Li, Bang Liu, Filip Ilievski, Daniel Garijo, Hans Chalupsky, Pedro Szekely, Ilias Kanellos, Dimitris Sacharidis, Thanasis Vergoulis, Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Srinivasan Sengamedu, Chandan K. Reddy, Friedhelm Victor, Bernhard Haslhofer, George Katsogiannis- Meimarakis, Georgia Koutrika, Shengmin Jin, Danai Koutra, Reza Zafarani, Yulia Tsvetkov, Vidhisha Balachandran, Sachin Kumar, Xiangyu Zhao, Bo Chen, Huifeng Guo, Yejing Wang, Ruiming Tang, Yang Zhang, Wenjie Wang, Peng Wu, Fuli Feng, and Xiangnan He

Abstract

This paper summarizes the content of the 20 tutorials that have been given at The Web Conference 2022: 85% of these tutorials are lecture style, and 15% of these are hands on. Published version

Published
2022

20. Overview of LifeCLEF 2022: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction

Author

Alexis Joly, Hervé Goëau, Stefan Kahl, Lukáš Picek, Titouan Lorieul, Elijah Cole, Benjamin Deneu, Maximilien Servajean, Andrew Durso, Hervé Glotin, Robert Planqué, Willem-Pier Vellinga, Amanda Navine, Holger Klinck, Tom Denton, Ivan Eggel, Pierre Bonnet, Milan Šulc, Marek Hrúz, Scientific Data Management (ZENITH), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), California Institute of Technology (CALTECH), ADVanced Analytics for data SciencE (ADVANSE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université Paul-Valéry - Montpellier 3 (UPVM), Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Xeno-canto foundation, University of Hawai'i [Hilo], Google LLC, University of West Bohemia [Plzeň ], Alberto Barrón-Cedeño, Giovanni Da San Martino, Mirko Degli Esposti, Fabrizio Sebastiani, Craig Macdonald, Gabriella Pasi, Allan Hanbury, Martin Potthast, Guglielmo Faggioli, Nicola Ferro, and European Project: 863463,Cos4Cloud project

Subjects
Large-scales, Living organisms, Biodiversity monitoring, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, Biodiversity conservation, Species Distribution Prediction, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, Species distributions, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Machine learning, LifeCLEF 2022, Monitoring system, Geographics, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Machine-learning, Species identification, General publics
Abstract

International audience; Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However, the difficulty of identifying plants, animals and fungi is hindering the aggregation of new data and knowledge. Identifying and naming living organisms is almost impossible for the general public and is often difficult even for professionals and naturalists. Bridging this gap is a key step towards enabling effective biodiversity monitoring systems. The LifeCLEF campaign, presented in this paper, has been promoting and evaluating advances in this domain since 2011. The 2022 edition proposes five data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: very large-scale plant identification, (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: remote sensing based prediction of species, (iv) SnakeCLEF: snake species identification on a global scale, and (v) FungiCLEF: fungi recognition as an open set classification problem. This paper overviews the motivation, methodology and main outcomes of that five challenges.

Published
2022
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