  Gender-inclusive language is important for achieving gender equality in
languages with gender inflections, such as German. While stirring some
controversy, it is increasingly adopted by companies and political
institutions. A handful of tools have been developed to help people use
gender-inclusive language by identifying instances of the generic masculine and
providing suggestions for more inclusive reformulations. In this report, we
define the underlying tasks in terms of natural language processing, and
present a dataset and measures for benchmarking them. We also present a model
that implements these tasks, by combining an inclusive language database with
an elaborate sequence of processing steps via standard pre-trained models. Our
model achieves a recall of 0.89 and a precision of 0.82 in our benchmark for
identifying exclusive language; and one of its top five suggestions is chosen
in real-world texts in 44% of cases. We sketch how the area could be further
advanced by training end-to-end models and using large language models; and we
urge the community to include more gender-inclusive texts in their training
data in order to not present an obstacle to the adoption of gender-inclusive
language. Through these efforts, we hope to contribute to restoring justice in
language and, to a small extent, in reality.
