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
- 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)
- PDF:
- https://preview.aclanthology.org/cschoel_rss_and_blog/2020.alw-1.4.pdf