User-Centric Gender Rewriting

TL;DR

This paper presents a novel multi-step system for gender rewriting in Arabic, effectively handling two-user contexts and improving personalization in machine translation outputs.

Contribution

It introduces a new approach combining rule-based and neural models for gender rewriting in Arabic, addressing two-user contexts and demonstrating improved accuracy.

Findings

Achieved 88.42 M2 F0.5 on a blind test set.

Improved over previous first-person-only gender rewriting methods by 3.05 in M2 F0.5.

Validated system's use in post-editing machine translation for personalized outputs.

Abstract

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.