%0 Conference Proceedings %T User-Centric Gender Rewriting %A Alhafni, Bashar %A Habash, Nizar %A Bouamor, Houda %Y Carpuat, Marine %Y de Marneffe, Marie-Catherine %Y Meza Ruiz, Ivan Vladimir %S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies %D 2022 %8 July %I Association for Computational Linguistics %C Seattle, United States %F alhafni-etal-2022-user %X In this paper, we define the task of gender rewriting in contexts involving two users (I and/or You) – first and second grammatical persons with independent grammatical gender preferences. We focus on Arabic, a gender-marking morphologically rich language. We develop a multi-step system that combines the positive aspects of both rule-based and neural rewriting models. Our results successfully demonstrate the viability of this approach on a recently created corpus for Arabic gender rewriting, achieving 88.42 M2 F0.5 on a blind test set. Our proposed system improves over previous work on the first-person-only version of this task, by 3.05 absolute increase in M2 F0.5. We demonstrate a use case of our gender rewriting system by using it to post-edit the output of a commercial MT system to provide personalized outputs based on the users’ grammatical gender preferences. We make our code, data, and pretrained models publicly available. %R 10.18653/v1/2022.naacl-main.46 %U https://aclanthology.org/2022.naacl-main.46 %U https://doi.org/10.18653/v1/2022.naacl-main.46 %P 618-631