Explainable Natural Language Processing

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.

* 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