JTML at SemEval-2019 Task 6: Offensive Tweets Identification using Convolutional Neural Networks

Johnny Torres, Carmen Vaca


Abstract
In this paper, we propose the use of a Convolutional Neural Network (CNN) to identify offensive tweets, as well as the type and target of the offense. We use an end-to-end model (i.e., no preprocessing) and fine-tune pre-trained embeddings (FastText) during training for learning words’ representation. We compare the proposed CNN model to a baseline model, such as Linear Regression, and several neural models. The results show that CNN outperforms other models, and stands as a simple but strong baseline in comparison to other systems submitted to the Shared Task.
Anthology ID:
S19-2117
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:
657–661
Language:
URL:
https://aclanthology.org/S19-2117
DOI:
10.18653/v1/S19-2117
Bibkey:
Cite (ACL):
Johnny Torres and Carmen Vaca. 2019. JTML at SemEval-2019 Task 6: Offensive Tweets Identification using Convolutional Neural Networks. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 657–661, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
JTML at SemEval-2019 Task 6: Offensive Tweets Identification using Convolutional Neural Networks (Torres & Vaca, SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2117.pdf