Fighting online hate speech is a challenge that is usually addressed using Natural Language Processing via automatic detection and removal of hate content. Besides this approach, counter narratives have emerged as an effective tool employed by NGOs to respond to online hate on social media platforms. For this reason, Natural Language Generation is currently being studied as a way to automatize counter narrative writing. However, the existing resources necessary to train NLG models are limited to 2-turn interactions (a hate speech and a counter narrative as response), while in real life, interactions can consist of multiple turns. In this paper, we present a hybrid approach for dialogical data collection, which combines the intervention of human expert annotators over machine generated dialogues obtained using 19 different configurations. The result of this work is DIALOCONAN, the first dataset comprising over 3000 fictitious multi-turn dialogues between a hater and an NGO operator, covering 6 targets of hate.

Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering / Bonaldi, Helena; Dellantonio, Sara; Tekiroglu, Serra Sinem; Guerini, Marco. - (2022), pp. 8031-8049. (Intervento presentato al convegno The Conference on Empirical Methods in Natural Language Processing (EMNLP) tenutosi a Abu Dhabi, United Arab Emirates nel 7th-11th December 2022).

Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering

Bonaldi Helena
Primo
;
Tekiroglu Serra Sinem
Penultimo
;
Guerini Marco
Ultimo
2022-01-01

Abstract

Fighting online hate speech is a challenge that is usually addressed using Natural Language Processing via automatic detection and removal of hate content. Besides this approach, counter narratives have emerged as an effective tool employed by NGOs to respond to online hate on social media platforms. For this reason, Natural Language Generation is currently being studied as a way to automatize counter narrative writing. However, the existing resources necessary to train NLG models are limited to 2-turn interactions (a hate speech and a counter narrative as response), while in real life, interactions can consist of multiple turns. In this paper, we present a hybrid approach for dialogical data collection, which combines the intervention of human expert annotators over machine generated dialogues obtained using 19 different configurations. The result of this work is DIALOCONAN, the first dataset comprising over 3000 fictitious multi-turn dialogues between a hater and an NGO operator, covering 6 targets of hate.
2022
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
209 N. Eighth Street, Stroudsburg PA 18360, USA
Association for Computational Linguistics
Bonaldi, Helena; Dellantonio, Sara; Tekiroglu, Serra Sinem; Guerini, Marco
Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering / Bonaldi, Helena; Dellantonio, Sara; Tekiroglu, Serra Sinem; Guerini, Marco. - (2022), pp. 8031-8049. (Intervento presentato al convegno The Conference on Empirical Methods in Natural Language Processing (EMNLP) tenutosi a Abu Dhabi, United Arab Emirates nel 7th-11th December 2022).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/370032
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