YNUWB at SemEval-2019 Task 6: K-max pooling CNN with average meta-embedding for identifying offensive language

Bin Wang, Xiaobing Zhou, Xuejie Zhang


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
Bibkey:
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)
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PDF:
https://aclanthology.org/S19-2143.pdf