01084nam0 22002771i 450 UON0002111620231205102019.34120020107d1958 |0itac50 bajpnJP|||| 1||||KentoshiKatsumi MoriTokyoShibundo1958 217 p. ; 15 cm001UON000136132001 Nihon rekishi shinsho117RELAZIONI INTERNAZIONALICina-GiapponeUONC003863FIJPTōkyōUONL000031GIA SERIEGIAPPONE - SERIEAMORI KatsumiUONV014311635797ShibundōUONV246744650ITSOL20240220RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00021116SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI GIA SERIE 035 117 SI SA 80679 7 117 RELAZIONI INTERNAZIONALI - GIAPPONE/CINARELAZIONI INTERNAZIONALI - Cina-GiapponeUONC001332Kentoshi1196728UNIOR04033nam 22006495 450 991084758990332120251210155100.03-031-51518-810.1007/978-3-031-51518-7(CKB)31403749500041(MiAaPQ)EBC31267110(Au-PeEL)EBL31267110(MiAaPQ)EBC31253958(Au-PeEL)EBL31253958(DE-He213)978-3-031-51518-7(EXLCZ)993140374950004120240405d2024 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierAccountable and Explainable Methods for Complex Reasoning over Text /by Pepa Atanasova1st ed. 2024.Cham :Springer Nature Switzerland :Imprint: Springer,2024.1 online resource (208 pages)3-031-51517-X 1. Executive Summary -- Part I: Accountability for Complex Reasoning Tasks over Text -- 2. Fact Checking with Insufficient Evidence -- 3. Generating Label Cohesive and Well-Formed Adversarial Claims -- Part II: Explainability for Complex Reasoning Tasks over Text -- 4. Generating Fact Checking Explanations -- 5. Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing -- 6. Multi-Hop Fact Checking of Political Claims -- Part III: Diagnostic Explainability Methods -- 7. A Diagnostic Study of Explainability Techniques for Text Classification -- 8. Diagnostics-Guided Explanation Generation -- 9. Recent Developments on Accountability and Explainability for Complex Reasoning Tasks.This thesis presents research that expands the collective knowledge in the areas of accountability and transparency of machine learning (ML) models developed for complex reasoning tasks over text. In particular, the presented results facilitate the analysis of the reasons behind the outputs of ML models and assist in detecting and correcting for potential harms. It presents two new methods for accountable ML models; advances the state of the art with methods generating textual explanations that are further improved to be fluent, easy to read, and to contain logically connected multi-chain arguments; and makes substantial contributions in the area of diagnostics for explainability approaches. All results are empirically tested on complex reasoning tasks over text, including fact checking, question answering, and natural language inference. This book is a revised version of the PhD dissertation written by the author to receive her PhD from the Faculty of Science, University of Copenhagen, Denmark. In 2023, it won the Informatics Europe Best Dissertation Award, granted to the most outstanding European PhD thesis in the field of computer science.Natural language processing (Computer science)Information storage and retrieval systemsMachine learningNatural Language Processing (NLP)Information Storage and RetrievalMachine LearningAprenentatge automàticthubTractament del llenguatge natural (Informàtica)thubSistemes d'informacióthubLlibres electrònicsthubNatural language processing (Computer science)Information storage and retrieval systems.Machine learning.Natural Language Processing (NLP).Information Storage and Retrieval.Machine Learning.Aprenentatge automàticTractament del llenguatge natural (Informàtica)Sistemes d'informació006.31Atanasova Pepa1736382MiAaPQMiAaPQMiAaPQBOOK9910847589903321Accountable and Explainable Methods for Complex Reasoning over Text4156229UNINA