@inproceedings{wang-etal-2019-ynuwb,
title = "{YNUWB} at {S}em{E}val-2019 Task 6: K-max pooling {CNN} with average meta-embedding for identifying offensive language",
author = "Wang, Bin and
Zhou, Xiaobing and
Zhang, Xuejie",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2143/",
doi = "10.18653/v1/S19-2143",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T YNUWB at SemEval-2019 Task 6: K-max pooling CNN with average meta-embedding for identifying offensive language
%A Wang, Bin
%A Zhou, Xiaobing
%A Zhang, Xuejie
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F wang-etal-2019-ynuwb
%X 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.
%R 10.18653/v1/S19-2143
%U https://aclanthology.org/S19-2143/
%U https://doi.org/10.18653/v1/S19-2143
%P 818-822
Markdown (Informal)
[YNUWB at SemEval-2019 Task 6: K-max pooling CNN with average meta-embedding for identifying offensive language](https://aclanthology.org/S19-2143/) (Wang et al., SemEval 2019)
ACL