Recently research has started focusing on avoiding undesired effects that come with content moderation, such as censorship and overblocking, when dealing with hatred online. The core idea is to directly intervene in the discussion with textual responses that are meant to counter the hate content and prevent it from further spreading. Accordingly, automation strategies, such as natural language generation, are beginning to be investigated. Still, they suffer from the lack of sufficient amount of quality data and tend to produce generic/repetitive responses. Being aware of the aforementioned limitations, we present a study on how to collect responses to hate effectively, employing large scale unsupervised language models such as GPT-2 for the generation of silver data, and the best annotation strategies/neural architectures that can be used for data filtering before expert validation/post-editing.

Generating Counter Narratives against Online Hate Speech: Data and Strategies / Tekiroglu, Ss; Chung, Yl; Guerini, M. - ELETTRONICO. - (2020), pp. 1177-1190. ((Intervento presentato al convegno Annual Meeting of the Association for Computational Linguistics tenutosi a online nel 5-10/7/2020 [10.18653/v1/2020.acl-main.110].

Generating Counter Narratives against Online Hate Speech: Data and Strategies

Tekiroglu, SS;Chung, YL;Guerini, M
2020

Abstract

Recently research has started focusing on avoiding undesired effects that come with content moderation, such as censorship and overblocking, when dealing with hatred online. The core idea is to directly intervene in the discussion with textual responses that are meant to counter the hate content and prevent it from further spreading. Accordingly, automation strategies, such as natural language generation, are beginning to be investigated. Still, they suffer from the lack of sufficient amount of quality data and tend to produce generic/repetitive responses. Being aware of the aforementioned limitations, we present a study on how to collect responses to hate effectively, employing large scale unsupervised language models such as GPT-2 for the generation of silver data, and the best annotation strategies/neural architectures that can be used for data filtering before expert validation/post-editing.
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
209 N EIGHTH STREET, STROUDSBURG, PA 18360 USA
ASSOC COMPUTATIONAL LINGUISTICS-ACL
Tekiroglu, Ss; Chung, Yl; Guerini, M
Generating Counter Narratives against Online Hate Speech: Data and Strategies / Tekiroglu, Ss; Chung, Yl; Guerini, M. - ELETTRONICO. - (2020), pp. 1177-1190. ((Intervento presentato al convegno Annual Meeting of the Association for Computational Linguistics tenutosi a online nel 5-10/7/2020 [10.18653/v1/2020.acl-main.110].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/296174
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