  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
models publicly available.
