KDEHatEval at SemEval-2019 Task 5: A Neural Network Model for Detecting Hate Speech in Twitter

Umme Aymun Siddiqua, Abu Nowshed Chy, Masaki Aono

Abstract
In the age of emerging volume of microblog platforms, especially twitter, hate speech propagation is now of great concern. However, due to the brevity of tweets and informal user generated contents, detecting and analyzing hate speech on twitter is a formidable task. In this paper, we present our approach for detecting hate speech in tweets defined in the SemEval-2019 Task 5. Our team KDEHatEval employs different neural network models including multi-kernel convolution (MKC), nested LSTMs (NLSTMs), and multi-layer perceptron (MLP) in a unified architecture. Moreover, we utilize the state-of-the-art pre-trained sentence embedding models including DeepMoji, InferSent, and BERT for effective tweet representation. We analyze the performance of our method and demonstrate the contribution of each component of our architecture.
Anthology ID:
S19-2064
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:
365–370
Language:
URL:
https://aclanthology.org/S19-2064
DOI:
10.18653/v1/S19-2064
Bibkey:
Cite (ACL):
Umme Aymun Siddiqua, Abu Nowshed Chy, and Masaki Aono. 2019. KDEHatEval at SemEval-2019 Task 5: A Neural Network Model for Detecting Hate Speech in Twitter. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 365–370, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
KDEHatEval at SemEval-2019 Task 5: A Neural Network Model for Detecting Hate Speech in Twitter (Siddiqua et al., SemEval 2019)
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PDF:
https://preview.aclanthology.org/autopr/S19-2064.pdf
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