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 gendermarking 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 M 2 F 0.5 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 M 2 F 0.5 . 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 . 1 Gender bias is a fundamental problem in natural language processing ( NLP ) and it has been receiving an increasing attention across a variety of core tasks such as machine translation ( MT ) , co-reference resolution , and dialogue systems . Research has shown that NLP systems have the ability to embed and amplify gender bias ( Sun et al . , 2019 ) , which not only degrades users ' experiences but also creates representational harm ( Blodgett et al . , 2020 ) . The embedded bias within NLP systems is usually attributed to training models on biased data that reflects the social inequalities of the world we live in . However , even the most balanced of models can still exhibit and amplify bias if they are designed to produce a single text output without taking their users ' gender preferences into consideration . Therefore , to provide the correct user-aware output , NLP systems should be designed to produce outputs that are as gender-specific as the users information they have access to . Users information could be either embedded as part of the input or provided externally by the users themselves . In cases where this information is unavailable to the system , generating all gender-specific forms or a gender-neutral form is more appropriate . Producing user-aware outputs becomes more challenging for systems targeting multi-user contexts ( first and second persons , with independent grammatical gender preferences ) , particularly when dealing with gender-marking morphologically rich languages . 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 and we focus on Arabic , a gender-marking morphologically rich language . The main contributions of our work are as follows : 1 . We introduce a multi-step gender rewriting system that combines the positive aspects of rule-based and neural models . 2 . We demonstrate our approach 's effectiveness by establishing a strong benchmark on a publicly available multi-user Arabic gender rewriting corpus . 3 . We show that our best system yields state-ofthe-art results on the first-person-only version of this task , beating previous work . 4 . We demonstrate a use case of our system by post-editing the output of an MT system to match users ' grammatical gender preferences . This paper is organized as follows . We first discuss related work ( § 2 ) as well as relevant Arabic linguistic facts ( § 3 ) . We then define the gender rewriting task in § 4 and describe the data we use and the gender rewriting model we build in § 5 and § 6 . Lastly , we present our experimental setup ( § 7 ) and results ( § 8) and conclude in § 9 . We defined the task of gender rewriting in contexts involving two users ( I and/or You ) , and developed a multi-step system that combines the positive aspects of both rule-based and neural rewriting models . Our best models establish the benchmark for this newly defined task and the SOTA for a previously defined first person version of it . We further demonstrated a use case of our gender rewriting system by post-editing the output of a commercial MT system to provide personalized outputs based on the users ' grammatical gender preferences . In future work , we plan to explore the use of other pretrained models , and to work on the problem of gender rewriting in other languages and dialectal varieties .

Challenge: Systems which can produce user-aware outputs to avoid embedding and amplifying gender bias remain a challenge, particularly with gender-marking morphologically rich languages.
Approach: They define the task of gender rewriting in contexts involving two users and propose a multi-step system that combines the rule-based and neural models.
Outcome: The proposed approach is shown to be a strong baseline on the Arabic gender rewriting corpus and outperforms the state-of-the-art of the first-person-only version.