Scmhl5 at TRAC-2 Shared Task on Aggression Identification: Bert Based Ensemble Learning Approach

Han Liu, Pete Burnap, Wafa Alorainy, Matthew Williams


Abstract
This paper presents a system developed during our participation (team name: scmhl5) in the TRAC-2 Shared Task on aggression identification. In particular, we participated in English Sub-task A on three-class classification (‘Overtly Aggressive’, ‘Covertly Aggressive’ and ‘Non-aggressive’) and English Sub-task B on binary classification for Misogynistic Aggression (‘gendered’ or ‘non-gendered’). For both sub-tasks, our method involves using the pre-trained Bert model for extracting the text of each instance into a 768-dimensional vector of embeddings, and then training an ensemble of classifiers on the embedding features. Our method obtained accuracy of 0.703 and weighted F-measure of 0.664 for Sub-task A, whereas for Sub-task B the accuracy was 0.869 and weighted F-measure was 0.851. In terms of the rankings, the weighted F-measure obtained using our method for Sub-task A is ranked in the 10th out of 16 teams, whereas for Sub-task B the weighted F-measure is ranked in the 8th out of 15 teams.
Anthology ID:
2020.trac-1.10
Volume:
Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
TRAC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
62–68
Language:
English
URL:
https://aclanthology.org/2020.trac-1.10
DOI:
Bibkey:
Cite (ACL):
Han Liu, Pete Burnap, Wafa Alorainy, and Matthew Williams. 2020. Scmhl5 at TRAC-2 Shared Task on Aggression Identification: Bert Based Ensemble Learning Approach. In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying, pages 62–68, Marseille, France. European Language Resources Association (ELRA).
Cite (Informal):
Scmhl5 at TRAC-2 Shared Task on Aggression Identification: Bert Based Ensemble Learning Approach (Liu et al., TRAC 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/2020.trac-1.10.pdf