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Statistical Significance Testing for Natural Language Processing (Paperback)
Loot Price: R1,665
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Statistical Significance Testing for Natural Language Processing (Paperback)
Series: Synthesis Lectures on Human Language Technologies
Expected to ship within 10 - 15 working days
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Data-driven experimental analysis has become the main evaluation
tool of Natural Language Processing (NLP) algorithms. In fact, in
the last decade, it 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 statistical
significance testing in NLP research: If we, as a community, rely
on empirical evaluation to validate our hypotheses and reveal the
correct language processing mechanisms, we better be sure that our
results are not coincidental. The goal of this book is to discuss
the main aspects of statistical significance testing in NLP. Our
guiding assumption throughout the book is that the basic question
NLP researchers and engineers deal with is whether or not one
algorithm can be considered better than another one. This question
drives the field forward as it allows the constant progress of
developing better technology for language processing challenges. In
practice, researchers and engineers would like to draw the right
conclusion from a limited set of experiments, and this conclusion
should hold for other experiments with datasets they do not have at
their disposal or that they cannot perform due to limited time and
resources. The book hence discusses the opportunities and
challenges in using statistical significance testing in NLP, from
the point of view of experimental comparison between two
algorithms. We cover topics such as choosing an appropriate
significance test for the major NLP tasks, dealing with the unique
aspects of significance testing for non-convex deep neural
networks, accounting for a large number of comparisons between two
NLP algorithms in a statistically valid manner (multiple hypothesis
testing), and, finally, the unique challenges yielded by the nature
of the data and practices of the field.
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