In this paper, we present the results and findings of the Shared Task on
Gender Rewriting, which was organized as part of the Seventh Arabic Natural
Language Processing Workshop. The task of gender rewriting refers to generating
alternatives of a given sentence to match different target user gender contexts
(e.g., female speaker with a male listener, a male speaker with a male
listener, etc.). This requires changing the grammatical gender (masculine or
feminine) of certain words referring to the users. In this task, we focus on
Arabic, a gender-marking morphologically rich language. A total of five teams
from four countries participated in the shared task.

本文介绍了第七届阿拉伯语自然语言处理研讨会上举办的 Gender Rewriting 共享任务的结果和发现，该任务需要生成给定句子的替代版本以匹配不同的目标用户性别背景（例如女性发言人与男性听众、男性发言人与男性听众等等），这需要改变涉及用户的某些词的语法性别（阳性或阴性）。在本任务中，我们专注于阿拉伯语，这是一种具有性别标记且词形丰富的语言，共有来自四个国家的五个团队参加了该共享任务。

性别重写共享任务

The Shared Task on Gender Rewriting

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.

该研究针对阿拉伯语的两个用户的上下文中的语法性别，开发了一个多步骤的系统，以结合基于规则和神经重写模型的正面方面。通过使用商业机器翻译系统的后处理输出，提供基于用户语法性别喜好的个性化输出。