Abstract
This paper describes the system submitted to SemEval 2019 Task 6: OffensEval 2019. The task aims to identify and categorize offensive language in social media, we only participate in Sub-task A, which aims to identify offensive language. In order to address this task, we propose a system based on a K-max pooling convolutional neural network model, and use an argument for averaging as a valid meta-embedding technique to get a metaembedding. Finally, we also use a cyclic learning rate policy to improve model performance. Our model achieves a Macro F1-score of 0.802 (ranked 9/103) in the Sub-task A.- Anthology ID:
- S19-2143
- 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:
- 818–822
- Language:
- URL:
- https://aclanthology.org/S19-2143
- DOI:
- 10.18653/v1/S19-2143
- Cite (ACL):
- Bin Wang, Xiaobing Zhou, and Xuejie Zhang. 2019. YNUWB at SemEval-2019 Task 6: K-max pooling CNN with average meta-embedding for identifying offensive language. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 818–822, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- YNUWB at SemEval-2019 Task 6: K-max pooling CNN with average meta-embedding for identifying offensive language (Wang et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/S19-2143.pdf