  Data augmentation is an important component in the robustness evaluation of
models in natural language processing (NLP) and in enhancing the diversity of
the data they are trained on. In this paper, we present NL-Augmenter, a new
participatory Python-based natural language augmentation framework which
supports the creation of both transformations (modifications to the data) and
filters (data splits according to specific features). We describe the framework
and an initial set of 117 transformations and 23 filters for a variety of
natural language tasks. We demonstrate the efficacy of NL-Augmenter by using
several of its transformations to analyze the robustness of popular natural
language models. The infrastructure, datacards and robustness analysis results
are available publicly on the NL-Augmenter repository
(https://github.com/GEM-benchmark/NL-Augmenter).
