@InProceedings{wang-zhou-zhang:2019:S19-2,
  author    = {Wang, Bin  and  Zhou, Xiaobing  and  Zhang, Xuejie},
  title     = {YNUWB at SemEval-2019 Task 6: K-max pooling CNN with average meta-embedding for identifying offensive language},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {818--822},
  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.},
  url       = {http://www.aclweb.org/anthology/S19-2143}
}