Explainable Natural Language Processing
Research output: Book/Report › Book › Research › peer-review
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.
Original language | English |
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Publisher | Morgan & Claypool |
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Number of pages | 123 |
DOIs | |
Publication status | Published - 2021 |
Series | Synthesis Lectures on Human Language Technologies |
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Number | 3 |
Volume | 14 |
ISSN | 1947-4040 |
ID: 299760182