Reseña del libro
Validity, Reliability, and Significance: Empirical Methods for NLP and
Data Science
Stefan Riezler and Michael Hagmann
(Heidelberg University)
morgan & Claypool Publishers (Synthesis Lectures on Human Language Technologies,
edited by Graeme Hirst, volumen 55), 2022, 147 páginas; hardcover, 9781636392738;
paperback, 9781636392714; ebook, 9781636392721;
doi:10.2200/S01137ED1V01Y202110HLT055
Reviewed by
Richard Futrell
Universidad de California, Irvine
When we come up with a new model in NLP and machine learning more generally, nosotros
usually look at some performance metric (one number), compare it against the same
performance metric for a strong baseline model (one number), and if the new model
gets a better number, we mark it in bold and declare it the winner. For anyone with
a background in statistics or a field where conclusions must be drawn on the basis of
noisy data, this procedure is frankly shocking. Suppose model A gets a BLEU score one
point higher than model B: Is that difference reliable? If you used a slightly different
dataset for training and evaluation, would that one point difference still hold? Would
the difference even survive running the same models on the same datasets but with dif-
ferent random seeds? In fields such as psychology and biology, it is standard to answer
such questions using standardized statistical procedures to make sure that differences
of interest are larger than some quantification of measurement noise. Making a claim
based on a bare difference of two numbers is unthinkable. Yet statistical procedures
remain rare in the evaluation of NLP models, whose performance metrics are arguably
just as noisy.
To these objections, NLP practitioners can respond that they have faithfully fol-
lowed the hallowed train-(dev-)test split paradigm. As long as proper test set discipline
has been followed, the theory goes, the evaluation is secure: By testing on held-out
datos, we can be sure that our models are performing well in a way that is independent
of random accidents of the training data, and by testing on that data only once, nosotros
guard against making claims based on differences that would not replicate if we ran the
models again. But does the train-test split paradigm really guard against all problems
of validity and reliability?
Into this situation comes the book under review, Validity, Reliability, and Significance:
Empirical Methods for NLP and Data Science, by Stefan Riezler and Michael Hagmann.
The authors argue that the train-test split paradigm does not in fact insulate NLP from
problems relating to the validity and reliability of its models, their features, and their
performance metrics. They present numerous case studies to prove their point, y
advocate and teach standard statistical methods as the solution, with rich examples
https://doi.org/10.1162/coli r 00467
© 2022 Asociación de Lingüística Computacional
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Ligüística computacional
Volumen 49, Número 1
of successful application to problems of NLP system evaluation and interpretation.
The statistical methods are presented with motivation from first principles, with deep
citations into the fundamental statistical literature and formal derivations presented
at the right level for an average NLP or ML practitioner to follow. The methods and
theory behind them are frequentist, and represent the current best practices within that
paradigma, based on linear mixed-effects models (LMEMs) and generalized additive
modelos (GAMs), and using generalized likelihood ratio tests for hypothesis testing. El
authors also discuss fundamental philosophical ideas including the theory of measure-
ment and construct validity, providing an overall deeper-than-average view into this
literature in a succinct, comprehensible form.
The book takes the form of a kind of taxonomy of problems that can occur in NLP
análisis, with each kind of problem paired with statistical solutions to diagnose and
treat it, along with case studies involving Natural Language Inference (NLI), biomedical
NLP, and Machine Translation (MONTE) evaluación, among others. Problems are divided
into Validity, Robustez, and Significance.
Validity (discussed in Chapter 2) refers to the extent to which a model captures real
generalizable predictive information based on input features. The authors divide feature
validity problems into three kinds: (1) bias features, which are spurious features that
are correlated with labels in the training data; (2) illegitimate features, where features
contain unexpected and non-generalizable information, such as numerical patient IDs
containing information about which hospital a patient was treated in, which in turn
contains information about what conditions the patient likely has; y (3) circular
características, which are features in the training data that deterministically define the label.
Notablemente, these issues are not guarded against by the train-test split paradigm. A pesar de
these three kinds of invalidity may seem difficult to distinguish, they can in fact be
cleanly separated by different statistical analyses, which the authors motivate after a
discussion of measurement theory and construct validity.
De estos, the most interesting analyses are those to diagnose illegitimate features
and circular features. Illegitimate features are cast as a form of failure of transforma-
tion invariance. Based on measurement theory, the relationships between features and
outcomes should be subject to certain symmetries: Por ejemplo, if patient IDs are to
be used as features in a model to predict medical conditions, then the effect of the
patient ID on outcomes should be invariant to bijections applied to the patient ID. Cualquier
failure of this invariance is an indication that illegitimate information is present in the
patient IDs: Por ejemplo, if some of the digits in the ID indicate which hospital the
patient was treated at. Circular features, por otro lado, are diagnosed through a
procedure involving fitting GAMs to predict labels given increasing amounts of data; en
the limit of large data, any circular feature will end up with a large weight in the GAM
while the rest of the feature weights go to zero. The authors call this the “nullification
criterion,” and give extensive examples of the application of these ideas using biomed-
ical examples.
Reliability (discussed in Chapter 3) is the extent to which a result is robust to
replication—for example, if a new set of annotators is tasked with labeling the same
datos, how much inter-annotator agreement will there be; or if a model is run again with
a different random seed or a perturbation of hyperparameters, how likely is it to give
the same performance. The authors critically discuss Krippendorf’s α as a measure of
intra-annotator reliability, and they discuss using bootstrapping to calculate confidence
intervals. The real meat of this chapter is the discussion of model-based reliability tests
based on LMEMs to perform Variance Component Analysis (focusing on the random
efectos). These methods are applied to study reliability of data annotation of MT output
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Reseñas de libros
in terms of error rate. Reliability of predictions with respect to hyperparameters is also
discussed extensively, with LMEM analysis using hyperparameters as predictors.
Significance (discussed in Chapter 4) is the extent to which a result provides ev-
idence against some null hypothesis, usually that there is no difference between two
modelos. The authors’ main goal in this chapter is to advocate a general likelihood ratio
test performed on LMEMs as the most general and effective significance test. The idea is
to compare a model class M1, consisting of models predicting some outcome given a set
of input features, against another model class M0, which predicts the same outcome
from all the same input features except for some held-out features F, representing
the null hypothesis that the features F have no effect. The goodness-of-fit of these
models is compared using a likelihood ratio test based on the best-fitting models within
each class, and the null hypothesis M0 can be rejected if the likelihood ratio falls in a
certain range. In addition to the generalized likelihood ratio test, the authors discuss
nonparameteric significance tests such as permutation tests, and include a great deal of
useful statistical background on topics such as the Central Limit Theorem and the basic
logic of significance testing.
One content area that NLP practitioners may be hoping for in this book, but which
they will not find, is Bayesian methods as opposed to frequentist methods. Within
psicología, over the last decade, there has been an increasing interest in Bayesian
approaches to statistical inference, fueled in part by perceived flaws in the frequen-
tist paradigm, Por ejemplo, (1) difficulties in interpreting frequentist statistics such as
p-values and confidence intervals, which can be counter-intuitive; y (2) an over-
reliance on significance thresholds for drawing conclusions which leads to “p-hacking,"
where a researcher performs data analyses in ways that are unscrupulously targeted
toward achieving significance thresholds. Without taking a stance on whether or not
these arguments have merit, I think it is worth pointing out that the current book does
not much engage with the arguments against frequentism that are currently on the rise,
seeing these issues as out of scope.
En general, the book provides a useful introduction to the universe of statistical testing
and measurement theory in a way that is immediately applicable for analysis of NLP
modelos. Many of the methods advocated here can be applied straightforwardly, yielding
immediate improvements in terms of our confidence in the validity and reliability of our
modelos. More fundamentally, I believe the field of NLP will benefit from engagement
with the underlying ideas about validity and reliability that have originated in fields
such as psychology; the book provides an ample introduction to these ideas that cuts
right to their core.
Richard Futrell is an Associate Professor of Language Science and Computer Science at the Univer-
sity of California, Irvine. His research focuses on language processing in humans and machines.
His e-mail address is rfutrell@uci.edu.
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