03519nam a22004575i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001500172072001700187072002300204072001600227100008500243245011700328250001800445264006900463300005600532336002600588337002600614338003600640347002400676505070900700520116501409650005902574650005402633650002902687650004602716650004602762650002902808710003402837773002602871776003602897776003602933856004602969912001403015912001503029999001703044978-3-031-51518-7DE-He21320251030111806.0cr nn 008mamaa240405s2024    sz |    s    |||| 0|eng d  a97830315151879978-3-031-51518-77 a10.1007/978-3-031-51518-72doi 4aQA76.9.N38 7aUYQL2bicssc 7aCOM0730002bisacsh 7aUYQL2thema1 aAtanasova, Pepa.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut97476610aAccountable and Explainable Methods for Complex Reasoning over Texth[electronic resource] /cby Pepa Atanasova.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXVIII, 199 p. 24 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 a1. 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.  aThis 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. 0aNatural language processing (Computer science).965131 0aInformation storage and retrieval systems.965568 0aMachine learning.95505914aNatural Language Processing (NLP).96616424aInformation Storage and Retrieval.96557024aMachine Learning.9550592 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303151517008iPrinted edition:z978303151519440uhttps://doi.org/10.1007/978-3-031-51518-7  aZDB-2-SCS  aZDB-2-SXCS  c58964d58964