Accountable and Explainable Methods for Complex Reasoning over Text
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 mo...
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| Natura: | Elettronico eBook |
| Lingua: | inglese |
| Pubblicazione: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Edizione: | 1st ed. 2024. |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/978-3-031-51518-7 |
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Sommario:
- 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.