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Titlebook: Explainable Natural Language Processing; Anders Søgaard Book 2021 Springer Nature Switzerland AG 2021

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发表于 2025-3-21 18:26:12 | 显示全部楼层 |阅读模式
书目名称Explainable Natural Language Processing
编辑Anders Søgaard
视频video
丛书名称Synthesis Lectures on Human Language Technologies
图书封面Titlebook: Explainable Natural Language Processing;  Anders Søgaard Book 2021 Springer Nature Switzerland AG 2021
描述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.
出版日期Book 2021
版次1
doihttps://doi.org/10.1007/978-3-031-02180-0
isbn_softcover978-3-031-01052-1
isbn_ebook978-3-031-02180-0Series ISSN 1947-4040 Series E-ISSN 1947-4059
issn_series 1947-4040
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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发表于 2025-3-21 20:46:22 | 显示全部楼层
Stochastic Lot Sizing Problems,to address which gates and attention heads are discriminative, and which are redundant and dispensable: examples would include sentences such as . or . The extent to which gates or attention heads can be removed, tells us a lot about the inner workings of a neural network.
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https://doi.org/10.1007/978-3-031-12737-3 can also be interpreted. If we consider . such clouds, we can quantify the extent to which they are structurally similar, for example. Or we can learn clusters of vectors and analyze the clusters manually, or use these to compute functions that enable us to extract influential data points for our test examples.
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Perspectives,ut unseen members of these classes, i.e., methods that we are not aware of or that will be proposed in the future. §12.1 presents a few observations that hold for all members of different classes of the taxonomy presented here. §12.2 discusses a few things that I did not cover in the taxonomy presented here.
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