Explainable Natural Language Processing

Abstract

Explainable Natural Language Processing (NLP) is an emerging field, which has received significant attention from the NLP community in the last few years. At its core is the need to explain the predictions of machine learning models, now more frequently deployed and used in sensitive areas such as healthcare and law. The rapid developments in the area of explainable NLP have led to somewhat disconnected groups of studies working on these areas. This disconnect results in researchers adopting various definitions for similar problems, while also in certain cases enabling the re-creation of previous research, highlighting the need for a unified framework for explainable NLP. Written by Anders Søgaard, this book provides the author’s convincing view of how we should first define explanations, and, secondly, how we should categorize explanations and the approaches that generate them, creating first and foremost a taxonomy and a unified framework for explainable NLP. As per the author, this will make it easier to relate studies and explanation methodologies in this field, with the aim of accelerating research. It is a brilliant book for both researchers starting to explore explainable NLP, but also for researchers with experience in this area, as it provides a holistic up-to-date view of the explainable NLP at the local and global level. The author conveniently and logically presents each chapter as a “problem” of explainable NLP, as such providing also a taxonomy of explainable NLP problem areas and current approaches to tackle them. Under each chapter, explanation methods are described in detail, beginning initially with “foundational” approaches (e.g., vanilla gradients) and building toward more complex ones (e.g., integrated gradients). To complement the theory and make this into a complete guide to explainable NLP, the author also describes evaluation approaches and provides a list of datasets and code repositories. As such, although the book requires some basic knowledge of NLP and Machine Learning to get started, it is nevertheless accessible to a large audience. This book is organized into thirteen chapters. In the first chapter the author introduces the problems associated with previously proposed taxonomies for explainable NLP. Chapter 2 follows by introducing popular machine learning architectures used in NLP, while also introducing the explanation taxonomy proposed in the book. Chapters