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Corrections to “A Deep Learning Framework for the Classification of Brazilian Coins”

IEEE Access 2024年 12卷 70000-70000页

作者： Debabrata Swain Viral Rupapara Amro Nour Santosh Satapathy Biswaranjan Acharya Shakti Mishra Ali Bostani Department of Computer Science and Engineering Pandit Deendayal Energy University Gandhinagar India College of Engineering and Applied Sciences American University of Kuwait Salmiya Kuwait Department of Information and Communication Technology Pandit Deendayal Energy University Gandhinagar India Department of Computer Engineering-AI & Big Data Analytics Marwadi University Rajkot Gujarat India

In the above article [1] , ChatGPT was used to improve the English writing in the abstract and in Section II: Literature Survey. As per IEEE PSPB policy, the use of content generated by ai in an article shall be disclosed in the acknowledgments section of any article submitted to an IEEE publication. The purpose of this correction is to address the lack of disclosure in the published article.

关键词： Surveys Deep learning Writing Chatbots Artificial intelligence

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Assessing Trustworthy ai in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

引用

IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy ai in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy ai' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an ai system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The ai system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the ai system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Towards Robust Waveform-Based Acoustic Models

arXiv

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arXiv 2021年

作者： Oglic, Dino Cvetkovic, Zoran Sollich, Peter Renals, Steve Yu, Bin The Applied Analytics and Ai Data Sciences and Ai BioPharmaceuticals R&d AstraZeneca CambridgeCB2 8PA United Kingdom The Department of Engineering King's College London LondonWC2R 2LS United Kingdom The Institute for Theoretical Physics University of Göttingen Göttingen37073 Germany The Center for Speech Technology Research University of Edinburgh EdinburghEH8 9AB United Kingdom The Departments of Statistics and Electrical Engineering and Computer Sciences Uc Berkeley BerkeleyCA94720 United States

We study the problem of learning robust acoustic models in adverse environments, characterized by a significant mismatch between training and test conditions. This problem is of paramount importance for the deployment of speech recognition systems that need to perform well in unseen environments. First, we characterize data augmentation theoretically as an instance of vicinal risk minimization, which aims at improving risk estimates during training by replacing the delta functions that define the empirical density over the input space with an approximation of the marginal population density in the vicinity of the training samples. More specifically, we assume that local neighborhoods centered at training samples can be approximated using a mixture of Gaussians, and demonstrate theoretically that this can incorporate robust inductive bias into the learning process. We then specify the individual mixture components implicitly via data augmentation schemes, designed to address common sources of spurious correlations in acoustic models. To avoid potential confounding effects on robustness due to information loss, which has been associated with standard feature extraction techniques (e.g., FBANK and MFCC features), we focus on the waveform-based setting. Our empirical results show that the approach can generalize to unseen noise conditions, with 150% relative improvement in out-of-distribution generalization compared to training using the standard risk minimization principle. Moreover, the results demonstrate competitive performance relative to models learned using a training sample designed to match the acoustic conditions characteristic of test utterances. Copyright © 2021, The Authors. All rights reserved.

关键词： Delta functions

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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P18.04.B SINGLE CELL TRANSCRIPTOMIC ANALYSIS IDENTIFIES DISTINCT INTRATUMORAL SUBCLONES IN MENINGIOMA

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Neuro-Oncology 2023年第SUPPLEMENT_2期25卷 ii122–ii122页

作者： Blume, C Tang, Y Dogan, H Ritter, M Dentro, S von Deimling, A Gerstung, M Schlesner, M Sahm, F Maas, S L N University Hospital Heidelberg Dept. of Neuropathology Heidelberg Germany German Cancer Research Center (DKFZ) German Consortium for Translational Cancer Research (DKTK) Clinical Cooperation Unit Neuropathology Heidelberg Germany Division of AI in Oncology German Cancer Research Center (DKFZ) Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Hinxton United Kingdom Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany Department of Pathology Leiden University Medical Center Leiden Netherlands Department of Pathology Erasmus Medical Center Rotterdam Netherlands

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OS02-03 Using a systems biology approach to construct adverse outcome pathway networks aligned with the FaiR principles

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Toxicology Letters 2024年 399卷 S58-S58页

作者： L. Ladeira A. Mazein M. Ostaszewski A. Verhoeven E. Kuchovská J. Sanz-Serrano A. Drees K. Reiche K. Sewald E. Fritsche M. Vinken L. Geris B. Staumont University of Liège GIGA Molecular & Computational Biology Liège Belgium University of Luxembourg Luxembourg Centre for Systems Biomedicine Belvaux Luxembourg ELIXIR Luxembourg Belvaux Luxembourg Vrije Universiteit Brussel IVTD research group Brussels Belgium IUF – Leibniz Research Institute for Environmental Medicine Dusseldorf Germany Fraunhofer Institute for Cell Therapy and Immunology Department of Diagnostics Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig Germany University Hospital University of Leipzig Institute of Clinical Immunology Medical Faculty Leipzig Germany DNTOX Dusseldorf Germany Swiss Centre for Applied Human Toxicology Basel Switzerland KU Leuven Skeletal Biology and Engineering Research Center Leuven Belgium KU Leuven Biomechanics Section Department of Mechanical Engineering Leuven Belgium

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Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCai 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Benchmarking in Optimization: Best Practice and Open Issues

arXiv

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arXiv 2020年

作者： Bartz-Beielstein, Thomas Doerr, Carola Bossek, Jakob Chandrasekaran, Sowmya Eftimov, Tome Fischbach, Andreas Kerschke, Pascal López-Ibáñez, Manuel Malan, Katherine M. Moore, Jason H. Naujoks, Boris Orzechowski, Patryk Volz, Vanessa Wagner, Markus Weise, Thomas Institute for Data Science Engineering and Analytics TH Köln Germany Sorbonne Université CNRS LIP6 Paris France Optimisation and Logistics School of Computer Science University of Adelaide Adelaide Australia Computer Systems Department Jožef Stefan Institute Ljubljana Slovenia Statistics and Optimization Group University of Münster Münster Germany School of Computer Science and Engineering University of Málaga Málaga Spain Department of Decision Sciences University of South Africa Pretoria South Africa Institute for Biomedical Informatics University of Pennsylvania PhiladelphiaPA United States Department of Automatics AGH University of Science and Technology Krakow Poland modl.ai Copenhagen Denmark Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei China

This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice in benchmarking is its main goal. The article discusses eight essential topics in benchmarking: clearly stated goals, well-specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility. The final goal is to provide well-accepted guidelines (rules) that might be useful for authors and reviewers. As benchmarking in optimization is an active and evolving field of research this manuscript is meant to co-evolve over time by means of periodic *** Codes 68W50. Copyright © 2020, The Authors. All rights reserved.

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NL-Augmenter A Framework for Task-Sensitive Natural Language Augmentation

arXiv

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arXiv 2021年

作者： Dhole, Kaustubh D. Gangal, Varun Gehrmann, Sebastian Gupta, Aadesh Li, Zhenhao Mahamood, Saad Mahendiran, Abinaya Mille, Simon Shrivastava, Ashish Tan, Samson Wu, Tongshuang Sohl-Dickstein, Jascha Choi, Jinho D. Hovy, Eduard Dušek, Ondřej Ruder, Sebastian Anand, Sajant Aneja, Nagender Banjade, Rabin Barthe, Lisa Behnke, Hanna Berlot-Attwell, Ian Boyle, Connor Brun, Caroline Sobrevilla Cabezudo, Marco Antonio Cahyawijaya, Samuel Chapuis, Emile Che, Wanxiang Choudhary, Mukund Clauss, Christian Colombo, Pierre Cornell, Filip Dagan, Gautier Das, Mayukh Dixit, Tanay Dopierre, Thomas Dray, Paul-Alexis Dubey, Suchitra Ekeinhor, Tatiana Giovanni, Marco Di Goyal, Tanya Gupta, Rishabh Hamla, Louanes Han, Sang Harel-Canada, Fabrice Honoré, Antoine Jindal, Ishan Joniak, Przemyslaw K. Kleyko, Denis Kovatchev, Venelin Krishna, Kalpesh Kumar, Ashutosh Langer, Stefan Lee, Seungjae Ryan Levinson, Corey James Liang, Hualou Liang, Kaizhao Liu, Zhexiong Lukyanenko, Andrey Marivate, Vukosi de Melo, Gerard Meoni, Simon Meyer, Maxime Mir, Afnan Moosavi, Nafise Sadat Muennighoff, Niklas Hon Mun, Timothy Sum Murray, Kenton Namysl, Marcin Obedkova, Maria Oli, Priti Pasricha, Nivranshu Pfister, Jan Plant, Richard Prabhu, Vinay Pais, Vasile Qin, Libo Raji, Shahab Rajpoot, Pawan Kumar Raunak, Vikas Rinberg, Roy Roberts, Nicholas Rodriguez, Juan Diego Roux, Claude Vasconcellos, P.H.S. Sai, Ananya B. Schmidt, Robin M. Scialom, Thomas Sefara, Tshephisho Shamsi, Saqib N. Shen, Xudong Shi, Yiwen Shi, Haoyue Shvets, Anna Siegel, Nick Sileo, Damien Simon, Jamie Singh, Chandan Sitelew, Roman Soni, Priyank Sorensen, Taylor Soto, William Srivastava, Aman Aditya Srivatsa, K.V. Sun, Tony Mukund Varma, T. Tabassum, A. Tan, Fiona Anting Teehan, Ryan Tiwari, Mo Tolkiehn, Marie Wang, Athena Wang, Zijian Wang, Zijie J. Wang, Gloria Wei, Fuxuan Wilie, Bryan Winata, Genta Indra Wu, Xinyi Wydmanski, Witold Xie, Tianbao Yaseen, Usama Yee, Michael A. Zhang, Jing Zhang, Yue ACKO Agara Amelia R&D New York United States Applied Research Laboratories The University of Texas at Austin United States Bloomberg Brigham Young University United States Carnegie Mellon University United States Center for Data and Computing in Natural Sciences Universität Hamburg Germany Charles River Analytics Charles University Prague Czech Republic Columbia University United States Council for Scientific and Industrial Research DeepMind United Kingdom Department of Computer Science University of Pretoria South Africa Drexel University United States Eberhard Karls University of Tübingen Germany Edinburgh Napier University United Kingdom Emory University United States Fablab by Inetum in Paris France Fraunhofer IAIS Germany Georgia Tech United States Google Brain United States Google Research United States Harbin Institute of Technology China Hasso Plattner Institute University of Potsdam Germany Hong Kong University of Science and Technology Hong Kong IBM Research IIT Delhi India IIT Madras India Illinois Mathematics and Science Academy United States Imperial College London United Kingdom Independent Indian Institute of Science Bangalore India Institut Teknologi Bandung Indonesia Institute of Data Science National University of Singapore Singapore International Institute of Information Technology Hyderabad India Jagiellonian University Poland Jean Monnet University France Johns Hopkins United States KTH Royal Institute of Technology Sweden KU Leuven Belgium MTS AI France Microsoft RedmondWA United States Mphasis NEXT Labs National University of Ireland Galway Ireland National University of Science and Technology Pakistan National University of Singapore Singapore Naver Labs Europe France Peking University China Politecnico di Milano University of Bologna Italy Polytechnic Institute of Paris France Pompeu Fabra University Spain Pontifical Catholic University of Minas Gerais Brazil Princeton University United States Rakuten India India Research Institu

data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Python-based natural language augmentation framework which supports the creation of both transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of natural language tasks. We demonstrate the efficacy of NL-Augmenter by using several of its tranformations to analyze the robustness of popular natural language models. The infrastructure, datacards and robutstness analysis results are available publicly on the NL-Augmenter repository (https://***/GEM-benchmark/NL-Augmenter). © 2021, CC BY.

关键词： Python

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