@inproceedings{alhafni-etal-2022-user,
title = "User-Centric Gender Rewriting",
author = "Alhafni, Bashar and
Habash, Nizar and
Bouamor, Houda",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.46/",
doi = "10.18653/v1/2022.naacl-main.46",
pages = "618--631",
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 pretrained models publicly available."
}
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<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 pretrained models publicly available.</abstract>
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%0 Conference Proceedings
%T User-Centric Gender Rewriting
%A Alhafni, Bashar
%A Habash, Nizar
%A Bouamor, Houda
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F alhafni-etal-2022-user
%X 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 pretrained models publicly available.
%R 10.18653/v1/2022.naacl-main.46
%U https://aclanthology.org/2022.naacl-main.46/
%U https://doi.org/10.18653/v1/2022.naacl-main.46
%P 618-631
Markdown (Informal)
[User-Centric Gender Rewriting](https://aclanthology.org/2022.naacl-main.46/) (Alhafni et al., NAACL 2022)
ACL
- Bashar Alhafni, Nizar Habash, and Houda Bouamor. 2022. User-Centric Gender Rewriting. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 618–631, Seattle, United States. Association for Computational Linguistics.