Work

User-Centric Gender Rewriting

Year: 2022

Type: conference-paper

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 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

Source: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)

DOI: 10.18653/v1/2022.naacl-main.46

Authors: Bashar Alhafni, Nizar Habash, Houda Bouamor

Institutions: New York University Abu Dhabi, Carnegie Mellon University Qatar, Carnegie Mellon University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Gender equality

Open Access status: hybrid

Funders: New York University Abu Dhabi, York University