TITLE: Automatic Construction of Evaluation Suites for Natural Language Generation Datasets 

ABSTRACT: Machine learning approaches applied to NLP are often evaluated by summarizing
their performance in a single number, for example accuracy. Since most test
sets are constructed as an i.i.d. sample from the overall data, this approach
overly simplifies the complexity of language and encourages overfitting to the
head of the data distribution. As such, rare language phenomena or text about
underrepresented groups are not equally included in the evaluation. To
encourage more in-depth model analyses, researchers have proposed the use of
multiple test sets, also called challenge sets, that assess specific
capabilities of a model. In this paper, we develop a framework based on this
idea which is able to generate controlled perturbations and identify subsets in
text-to-scalar, text-to-text, or data-to-text settings. By applying this
framework to the GEM generation benchmark, we propose an evaluation suite made
of 80 challenge sets, demonstrate the kinds of analyses that it enables and
shed light onto the limits of current generation models.
Machine learning approaches applied to NLP are often evaluated by summarizing
their performance in a single number, for example accuracy. Since most test
sets are constructed as an i.i.d. sample from the overall data, this approach
overly simplifies the complexity of language and encourages overfitting to the
head of the data distribution. As such, rare language phenomena or text about
underrepresented groups are not equally included in the evaluation. To
encourage more in-depth model analyses, researchers have proposed the use of
multiple test sets, also called challenge sets, that assess specific
capabilities of a model. In this paper, we develop a framework based on this
idea which is able to generate controlled perturbations and identify subsets in
text-to-scalar, text-to-text, or data-to-text settings. By applying this
framework to the GEM generation benchmark, we propose an evaluation suite made
of 80 challenge sets, demonstrate the kinds of analyses that it enables and
shed light onto the limits of current generation models.
Automatic Construction of Evaluation Suites for Natural Language Generation Datasets
http://arxiv.org/abs/2106.09069v1