HHU at SemEval-2019 Task 6: Context Does Matter - Tackling Offensive Language Identification and Categorization with ELMo

Alexander Oberstrass, Julia Romberg, Anke Stoll, Stefan Conrad


Abstract
We present our results for OffensEval: Identifying and Categorizing Offensive Language in Social Media (SemEval 2019 - Task 6). Our results show that context embeddings are important features for the three different sub-tasks in connection with classical machine and with deep learning. Our best model reached place 3 of 75 in sub-task B with a macro F1 of 0.719. Our approaches for sub-task A and C perform less well but could also deliver promising results.
Anthology ID:
S19-2112
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:
628–634
Language:
URL:
https://aclanthology.org/S19-2112
DOI:
10.18653/v1/S19-2112
Bibkey:
Cite (ACL):
Alexander Oberstrass, Julia Romberg, Anke Stoll, and Stefan Conrad. 2019. HHU at SemEval-2019 Task 6: Context Does Matter - Tackling Offensive Language Identification and Categorization with ELMo. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 628–634, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
HHU at SemEval-2019 Task 6: Context Does Matter - Tackling Offensive Language Identification and Categorization with ELMo (Oberstrass et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2112.pdf
Data
Hate Speech and Offensive Language