INCLUSIFY: A benchmark and a model for gender-inclusive German

TL;DR

This paper introduces INCLUSIFY, a benchmark and model for detecting and suggesting gender-inclusive language in German, aiming to promote gender equality through NLP tools.

Contribution

It defines NLP tasks for gender-inclusive language, provides a dataset and benchmark, and presents a model combining a database with pre-trained models for improved identification and reformulation.

Findings

Recall of 0.89 and precision of 0.82 in identifying exclusive language

44% of real-world texts had a top suggestion chosen

Proposes future directions with end-to-end models and large language models

Abstract

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.