LEADER 03708nam 22005655 450 001 9910847589903321 005 20240406124731.0 010 $a3-031-51518-8 024 7 $a10.1007/978-3-031-51518-7 035 $a(CKB)31403749500041 035 $a(MiAaPQ)EBC31267110 035 $a(Au-PeEL)EBL31267110 035 $a(MiAaPQ)EBC31253958 035 $a(Au-PeEL)EBL31253958 035 $a(DE-He213)978-3-031-51518-7 035 $a(EXLCZ)9931403749500041 100 $a20240405d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAccountable and Explainable Methods for Complex Reasoning over Text$b[electronic resource] /$fby Pepa Atanasova 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (208 pages) 311 $a3-031-51517-X 327 $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. 330 $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. 606 $aNatural language processing (Computer science) 606 $aInformation storage and retrieval systems 606 $aMachine learning 606 $aNatural Language Processing (NLP) 606 $aInformation Storage and Retrieval 606 $aMachine Learning 615 0$aNatural language processing (Computer science). 615 0$aInformation storage and retrieval systems. 615 0$aMachine learning. 615 14$aNatural Language Processing (NLP). 615 24$aInformation Storage and Retrieval. 615 24$aMachine Learning. 676 $a006.35 700 $aAtanasova$b Pepa$01736382 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910847589903321 996 $aAccountable and Explainable Methods for Complex Reasoning over Text$94156229 997 $aUNINA