11/30/2023 0 Comments Check emoji textHatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-Based Hate Both HatemojiCheck and HatemojiBuild are made publicly available.", Models built with these 5,912 adversarial examples perform substantially better at detecting emoji-based hate, while retaining strong performance on text-only hate. To address these weaknesses, we create the HatemojiBuild dataset using a human-and-model-in-the-loop approach. Using the test suite, we expose weaknesses in existing hate detection models. We present HatemojiCheck, a test suite of 3,930 short-form statements that allows us to evaluate performance on hateful language expressed with emoji. Emoji-based hate is an emerging challenge for automated detection. Publisher = "Association for Computational Linguistics",Ībstract = "Detecting online hate is a complex task, and low-performing models have harmful consequences when used for sensitive applications such as content moderation. Cite (Informal): Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-Based Hate (Kirk et al., NAACL 2022) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Video: Code HannahKirk/Hatemoji Data = "atemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-Based Hate",īooktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", Association for Computational Linguistics. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1352–1368, Seattle, United States. Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-Based Hate. Anthology ID: 2022.naacl-main.97 Volume: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies Month: July Year: 2022 Address: Seattle, United States Venue: NAACL SIG: Publisher: Association for Computational Linguistics Note: Pages: 1352–1368 Language: URL: DOI: 10.18653/v1/2022.naacl-main.97 Bibkey: kirk-etal-2022-hatemoji Cite (ACL): Hannah Kirk, Bertie Vidgen, Paul Rottger, Tristan Thrush, and Scott Hale. Both HatemojiCheck and HatemojiBuild are made publicly available. Abstract Detecting online hate is a complex task, and low-performing models have harmful consequences when used for sensitive applications such as content moderation.
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