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- W4287888545 abstract "In recent years, plenty of work has been done by the NLP community regarding gender bias detection and mitigation in language systems. Yet, to our knowledge, no one has focused on the difficult task of heteronormative language detection and mitigation. We consider this an urgent issue, since language technologies are growing increasingly present in the world and, as it has been proven by various studies, NLP systems with biases can create real-life adverse consequences for women, gender minorities and racial minorities and queer people. For these reasons, we propose and evaluate HeteroCorpus; a corpus created specifically for studying heterononormative language in English. Additionally, we propose a baseline set of classification experiments on our corpus, in order to show the performance of our corpus in classification tasks." @default.
- W4287888545 created "2022-07-26" @default.
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- W4287888545 date "2022-01-01" @default.
- W4287888545 modified "2023-09-27" @default.
- W4287888545 title "HeteroCorpus: A Corpus for Heteronormative Language Detection" @default.
- W4287888545 doi "https://doi.org/10.18653/v1/2022.gebnlp-1.23" @default.
- W4287888545 hasPublicationYear "2022" @default.
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