Dhole, Kaustubh

Gangal, Varun Prashant

Gehrmann, Sebastian

Kale, Mihir

Mahamood, Saad

Mille, Simon

Miltenburg, Emiel van

Perez-Beltrachini, Laura

English

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 develop evaluation suites made of 80 challenge sets, demonstrate the kinds of analyses that it enables, and shed light onto the limits of current generation models

Tilburg University Repository

Automatic Construction of Evaluation Suites for Natural Language Generation Datasets

https://pure.uvt.nl/ws/files/60417841/NeurIPS_Datasets_and_Benchmarks_2021_automatic_construction_of_evaluation_suites_for_natural_language_generation_datasets_Paper_round1.pdf

Automatic Construction of Evaluation Suites for Natural Language Generation Datasets

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