Reasons humbly offered to the honourable the House of Commons: shewing, why an additional duty should not be laid on all sail-cloath imported [[electronic resource]]

• Record Nr.: UNISA996394456303316
• Pubbl/distr/stampa: [London, : s.n., 1696?]
• Descrizione fisica: 1 sheet ([1] p.)
• Soggetti: Foreign trade regulation; Linen industry - England
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Imprint from Wing.; Reproduction of original in the British Library.
• Sommario/riassunto: eebo-0018

Legal Services Corporation [[electronic resource] ] : improved internal controls needed in grants management and oversight : report to congressional requesters

• Record Nr.: UNINA9910696348103321
• Pubbl/distr/stampa: [Washington, D.C.] : , : U.S. Govt. Accountability Office, , [2007]
• Descrizione fisica: ii, 36 pages : digital, PDF file
• Soggetti: Grants-in-aid - Management
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Title from title screen (viewed on Jan. 22, 2008).; "December 2007."; Paper version available from: U.S. Govt. Accountability Office, 441 G St., NW, Rm. LM, Washington, D.C. 20548.; "GAO-08-37."
• Nota di bibliografia: Includes bibliographical references.

Accountable and Explainable Methods for Complex Reasoning over Text / / by Pepa Atanasova

• Record Nr.: UNINA9910847589903321
• Autore: Atanasova Pepa
• Pubbl/distr/stampa: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• ISBN: 3-031-51518-8
• Edizione: [1st ed. 2024.]
• Descrizione fisica: 1 online resource (208 pages)
• Disciplina: 006.31
• Soggetti: Natural language processing (Computer science); Information storage and retrieval systems; Machine learning; Natural Language Processing (NLP); Information Storage and Retrieval; Machine Learning; Aprenentatge automàtic; Tractament del llenguatge natural (Informàtica); Sistemes d'informació; Llibres electrònics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: 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.
• Sommario/riassunto: 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.