@inproceedings{manolescu-etal-2019-tueval,
title = "{T}u{E}val at {S}em{E}val-2019 Task 5: {LSTM} Approach to Hate Speech Detection in {E}nglish and {S}panish",
author = {Manolescu, Mihai and
L{\"o}fflad, Denise and
Mohamed Saber, Adham Nasser and
Moradipour Tari, Masoumeh},
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/S19-2089",
doi = "10.18653/v1/S19-2089",
pages = "498--502",
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.",
}
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%0 Conference Proceedings
%T TuEval at SemEval-2019 Task 5: LSTM Approach to Hate Speech Detection in English and Spanish
%A Manolescu, Mihai
%A Löfflad, Denise
%A Mohamed Saber, Adham Nasser
%A Moradipour Tari, Masoumeh
%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 manolescu-etal-2019-tueval
%X 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.
%R 10.18653/v1/S19-2089
%U https://aclanthology.org/S19-2089
%U https://doi.org/10.18653/v1/S19-2089
%P 498-502
Markdown (Informal)
[TuEval at SemEval-2019 Task 5: LSTM Approach to Hate Speech Detection in English and Spanish](https://aclanthology.org/S19-2089) (Manolescu et al., SemEval 2019)
ACL