Titlebook: Statistical Significance Testing for Natural Language Processing; Rotem Dror,Lotem Peled-Cohen,Roi Reichart Book 2020 Springer Nature Swit

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Book 2020it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistic

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Deep Significance,ecisions about model design were usually limited to feature selection and the selection of one of a few loss functions. Consequently, when one model performed better than another on unseen data it was safe to argue that the winning model was generally better, especially when the results were statistically significant.

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Statistical Significance in NLP,tioned NLP tasks and measures with their suitable statistical significance tests. Last, we shortly discuss a recent practical issue that many researchers encounter when wanting to apply the statistical significance testing framework with big testsets.

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Statistical Significance in NLP,l as the properties of the actual significance tests. We now wish to continue exploring these notions in view of the NLP domain. We begin by diving into the world of NLP, presenting various tasks and their corresponding evaluation measures. We then provide a simple decision tree that helps guide the

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Deep Significance,[2003], and Ritter et al. [2011]. Hence, their training was often deterministic and the number of configurations a model could have was rather small—decisions about model design were usually limited to feature selection and the selection of one of a few loss functions. Consequently, when one model p