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        <namePart type="given">Amanda</namePart>
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            <namePart type="given">Marie-Francine</namePart>
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            <namePart type="given">Xuanjing</namePart>
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    <abstract>We present the first English corpus study on abusive language towards three conversational AI systems gathered ‘in the wild’: an open-domain social bot, a rule-based chatbot, and a task-based system. To account for the complexity of the task, we take a more ‘nuanced’ approach where our ConvAI dataset reflects fine-grained notions of abuse, as well as views from multiple expert annotators. We find that the distribution of abuse is vastly different compared to other commonly used datasets, with more sexually tinted aggression towards the virtual persona of these systems. Finally, we report results from bench-marking existing models against this data. Unsurprisingly, we find that there is substantial room for improvement with F1 scores below 90%.</abstract>
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%0 Conference Proceedings
%T ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI
%A Cercas Curry, Amanda
%A Abercrombie, Gavin
%A Rieser, Verena
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F cercas-curry-etal-2021-convabuse
%X We present the first English corpus study on abusive language towards three conversational AI systems gathered ‘in the wild’: an open-domain social bot, a rule-based chatbot, and a task-based system. To account for the complexity of the task, we take a more ‘nuanced’ approach where our ConvAI dataset reflects fine-grained notions of abuse, as well as views from multiple expert annotators. We find that the distribution of abuse is vastly different compared to other commonly used datasets, with more sexually tinted aggression towards the virtual persona of these systems. Finally, we report results from bench-marking existing models against this data. Unsurprisingly, we find that there is substantial room for improvement with F1 scores below 90%.
%R 10.18653/v1/2021.emnlp-main.587
%U https://aclanthology.org/2021.emnlp-main.587/
%U https://doi.org/10.18653/v1/2021.emnlp-main.587
%P 7388-7403

Markdown (Informal)

[ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI](https://aclanthology.org/2021.emnlp-main.587/) (Cercas Curry et al., EMNLP 2021)

• ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI (Cercas Curry et al., EMNLP 2021)

ACL

• Amanda Cercas Curry, Gavin Abercrombie, and Verena Rieser. 2021. ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7388–7403, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.