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        <title>Gender-Aware Reinflection using Linguistically Enhanced Neural Models</title>
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    <name type="personal">
        <namePart type="given">Bashar</namePart>
        <namePart type="family">Alhafni</namePart>
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    <name type="personal">
        <namePart type="given">Houda</namePart>
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            <title>Proceedings of the Second Workshop on Gender Bias in Natural Language Processing</title>
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            <namePart type="given">Marta</namePart>
            <namePart type="given">R</namePart>
            <namePart type="family">Costa-jussà</namePart>
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        <name type="personal">
            <namePart type="given">Christian</namePart>
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            <namePart type="given">Kellie</namePart>
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    <abstract>In this paper, we present an approach for sentence-level gender reinflection using linguistically enhanced sequence-to-sequence models. Our system takes an Arabic sentence and a given target gender as input and generates a gender-reinflected sentence based on the target gender. We formulate the problem as a user-aware grammatical error correction task and build an encoder-decoder architecture to jointly model reinflection for both masculine and feminine grammatical genders. We also show that adding linguistic features to our model leads to better reinflection results. The results on a blind test set using our best system show improvements over previous work, with a 3.6% absolute increase in M2 F0.5.</abstract>
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%0 Conference Proceedings
%T Gender-Aware Reinflection using Linguistically Enhanced Neural Models
%A Alhafni, Bashar
%A Habash, Nizar
%A Bouamor, Houda
%Y Costa-jussà, Marta R.
%Y Hardmeier, Christian
%Y Radford, Will
%Y Webster, Kellie
%S Proceedings of the Second Workshop on Gender Bias in Natural Language Processing
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F alhafni-etal-2020-gender
%X In this paper, we present an approach for sentence-level gender reinflection using linguistically enhanced sequence-to-sequence models. Our system takes an Arabic sentence and a given target gender as input and generates a gender-reinflected sentence based on the target gender. We formulate the problem as a user-aware grammatical error correction task and build an encoder-decoder architecture to jointly model reinflection for both masculine and feminine grammatical genders. We also show that adding linguistic features to our model leads to better reinflection results. The results on a blind test set using our best system show improvements over previous work, with a 3.6% absolute increase in M2 F0.5.
%U https://aclanthology.org/2020.gebnlp-1.12
%P 139-150

Markdown (Informal)

[Gender-Aware Reinflection using Linguistically Enhanced Neural Models](https://aclanthology.org/2020.gebnlp-1.12) (Alhafni et al., GeBNLP 2020)

• Gender-Aware Reinflection using Linguistically Enhanced Neural Models (Alhafni et al., GeBNLP 2020)

ACL

• Bashar Alhafni, Nizar Habash, and Houda Bouamor. 2020. Gender-Aware Reinflection using Linguistically Enhanced Neural Models. In Proceedings of the Second Workshop on Gender Bias in Natural Language Processing, pages 139–150, Barcelona, Spain (Online). Association for Computational Linguistics.