Fine-tuning for multi-domain and multi-label uncivil language detection

Kadir Bulut Ozler, Kate Kenski, Steve Rains, Yotam Shmargad, Kevin Coe, Steven Bethard


Abstract
Incivility is a problem on social media, and it comes in many forms (name-calling, vulgarity, threats, etc.) and domains (microblog posts, online news comments, Wikipedia edits, etc.). Training machine learning models to detect such incivility must handle the multi-label and multi-domain nature of the problem. We present a BERT-based model for incivility detection and propose several approaches for training it for multi-label and multi-domain datasets. We find that individual binary classifiers outperform a joint multi-label classifier, and that simply combining multiple domains of training data outperforms other recently-proposed fine tuning strategies. We also establish new state-of-the-art performance on several incivility detection datasets.
Anthology ID:
2020.alw-1.4
Volume:
Proceedings of the Fourth Workshop on Online Abuse and Harms
Month:
November
Year:
2020
Address:
Online
Editors:
Seyi Akiwowo, Bertie Vidgen, Vinodkumar Prabhakaran, Zeerak Waseem
Venue:
ALW
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
28–33
Language:
URL:
https://aclanthology.org/2020.alw-1.4
DOI:
10.18653/v1/2020.alw-1.4
Bibkey:
Cite (ACL):
Kadir Bulut Ozler, Kate Kenski, Steve Rains, Yotam Shmargad, Kevin Coe, and Steven Bethard. 2020. Fine-tuning for multi-domain and multi-label uncivil language detection. In Proceedings of the Fourth Workshop on Online Abuse and Harms, pages 28–33, Online. Association for Computational Linguistics.
Cite (Informal):
Fine-tuning for multi-domain and multi-label uncivil language detection (Ozler et al., ALW 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.alw-1.4.pdf
Video:
 https://slideslive.com/38939522