UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs

Jian Zhu, Zuoyu Tian, Sandra Kübler


Abstract
This paper describes the UM-IU@LING’s system for the SemEval 2019 Task 6: Offens-Eval. We take a mixed approach to identify and categorize hate speech in social media. In subtask A, we fine-tuned a BERT based classifier to detect abusive content in tweets, achieving a macro F1 score of 0.8136 on the test data, thus reaching the 3rd rank out of 103 submissions. In subtasks B and C, we used a linear SVM with selected character n-gram features. For subtask C, our system could identify the target of abuse with a macro F1 score of 0.5243, ranking it 27th out of 65 submissions.
Anthology ID:
S19-2138
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
788–795
Language:
URL:
https://aclanthology.org/S19-2138
DOI:
10.18653/v1/S19-2138
Bibkey:
Cite (ACL):
Jian Zhu, Zuoyu Tian, and Sandra Kübler. 2019. UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 788–795, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs (Zhu et al., SemEval 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/S19-2138.pdf
Code
 zytian9/SemEval-2019-Task-6