TuEval at SemEval-2019 Task 5: LSTM Approach to Hate Speech Detection in English and Spanish

Mihai Manolescu, Denise Löfflad, Adham Nasser Mohamed Saber, Masoumeh Moradipour Tari


Abstract
The detection of hate speech, especially in online platforms and forums, is quickly becoming a hot topic as anti-hate speech legislation begins to be applied to public discourse online. The HatEval shared task was created with this in mind; participants were expected to develop a model capable of determining whether or not input (in this case, Twitter datasets in English and Spanish) could be considered hate speech (designated as Task A), if they were aggressive, and whether the tweet was targeting an individual, or speaking generally (Task B). We approached this task by creating an LSTM model with an embedding layer. We found that our model performed considerably better on English language input when compared to Spanish language input. In English, we achieved an F1-Score of 0.466 for Task A and 0.462 for Task B; In Spanish, we achieved scores of 0.617 and 0.612 on Task A and Task B, respectively.
Anthology ID:
S19-2089
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
498–502
Language:
URL:
https://aclanthology.org/S19-2089
DOI:
10.18653/v1/S19-2089
Bibkey:
Cite (ACL):
Mihai Manolescu, Denise Löfflad, Adham Nasser Mohamed Saber, and Masoumeh Moradipour Tari. 2019. TuEval at SemEval-2019 Task 5: LSTM Approach to Hate Speech Detection in English and Spanish. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 498–502, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
TuEval at SemEval-2019 Task 5: LSTM Approach to Hate Speech Detection in English and Spanish (Manolescu et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2089.pdf