@inproceedings{sodhi-etal-2021-jibes,
title = "Jibes {\&} Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse",
author = "Sodhi, Ravsimar and
Pant, Kartikey and
Mamidi, Radhika",
editor = "Mostafazadeh Davani, Aida and
Kiela, Douwe and
Lambert, Mathias and
Vidgen, Bertie and
Prabhakaran, Vinodkumar and
Waseem, Zeerak",
booktitle = "Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.woah-1.14",
doi = "10.18653/v1/2021.woah-1.14",
pages = "132--139",
abstract = "Online abuse and offensive language on social media have become widespread problems in today{'}s digital age. In this paper, we contribute a Reddit-based dataset, consisting of 68,159 insults and 51,102 compliments targeted at individuals instead of targeting a particular community or race. Secondly, we benchmark multiple existing state-of-the-art models for both classification and unsupervised style transfer on the dataset. Finally, we analyse the experimental results and conclude that the transfer task is challenging, requiring the models to understand the high degree of creativity exhibited in the data.",
}
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%0 Conference Proceedings
%T Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse
%A Sodhi, Ravsimar
%A Pant, Kartikey
%A Mamidi, Radhika
%Y Mostafazadeh Davani, Aida
%Y Kiela, Douwe
%Y Lambert, Mathias
%Y Vidgen, Bertie
%Y Prabhakaran, Vinodkumar
%Y Waseem, Zeerak
%S Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F sodhi-etal-2021-jibes
%X Online abuse and offensive language on social media have become widespread problems in today’s digital age. In this paper, we contribute a Reddit-based dataset, consisting of 68,159 insults and 51,102 compliments targeted at individuals instead of targeting a particular community or race. Secondly, we benchmark multiple existing state-of-the-art models for both classification and unsupervised style transfer on the dataset. Finally, we analyse the experimental results and conclude that the transfer task is challenging, requiring the models to understand the high degree of creativity exhibited in the data.
%R 10.18653/v1/2021.woah-1.14
%U https://aclanthology.org/2021.woah-1.14
%U https://doi.org/10.18653/v1/2021.woah-1.14
%P 132-139
Markdown (Informal)
[Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse](https://aclanthology.org/2021.woah-1.14) (Sodhi et al., WOAH 2021)
ACL