Explainable Natural Language Processing

Anders Søgaard

• Publisher: Springer
• Year: 2022
• ISBN:
  9783031021800
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

This book presents a taxonomy framework and survey of methods relevant to explaining the decisions and analyzing the inner workings of Natural Language Processing (NLP) models. The book is intended to provide a snapshot of Explainable NLP, though the field continues to rapidly grow. The book is intended to be both readable by first-year M.Sc. students and interesting to an expert audience. The book opens by motivating a focus on providing a consistent taxonomy, pointing out inconsistencies and redundancies in previous taxonomies. It goes on to present (i) a taxonomy or framework for thinking about how approaches to explainable NLP relate to one another; (ii) brief surveys of each of the classes in the taxonomy, with a focus on methods that are relevant for NLP; and (iii) a discussion of the inherent limitations of some classes of methods, as well as how to best evaluate them. Finally, the book closes by providing a list of resources for further research on explainability.

Table of contents

• Cover
• Copyright Page
• Title Page
• Contents
• Acknowledgments
• Introduction
• A Framework for Explainable NLP
• Local-Backward Explanations
• Global-Backward Explanations
• Local-Forward Explanations of Intermediate Representations
• Global-Forward Explanations of Intermediate Representations
• Local-Forward Explanations of Continuous Output
• Global-Forward Explanations of Continuous Output
• Local-Forward Explanations of Discrete Output
• Global-Forward Explanations of Discrete Output
• Evaluating Explanations
• Perspectives
• Resources
• Bibliography
• Author's Biography