Abstract
This document describes the submission of team YNU-HPCC to SemEval-2019 for three Sub-tasks of Task 6: Sub-task A, Sub-task B, and Sub-task C. We have submitted four systems to identify and categorise offensive language. The first subsystem is an attention-based 2-layer bidirectional long short-term memory (BiLSTM). The second subsystem is a voting ensemble of four different deep learning architectures. The third subsystem is a stacking ensemble of four different deep learning architectures. Finally, the fourth subsystem is a bidirectional encoder representations from transformers (BERT) model. Among our models, in Sub-task A, our first subsystem performed the best, ranking 16th among 103 teams; in Sub-task B, the second subsystem performed the best, ranking 12th among 75 teams; in Sub-task C, the fourth subsystem performed best, ranking 4th among 65 teams.- Anthology ID:
- S19-2142
- 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:
- 812–817
- Language:
- URL:
- https://aclanthology.org/S19-2142
- DOI:
- 10.18653/v1/S19-2142
- Cite (ACL):
- Chengjin Zhou, Jin Wang, and Xuejie Zhang. 2019. YNU-HPCC at SemEval-2019 Task 6: Identifying and Categorising Offensive Language on Twitter. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 812–817, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- YNU-HPCC at SemEval-2019 Task 6: Identifying and Categorising Offensive Language on Twitter (Zhou et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/S19-2142.pdf