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Hate Speech Detection in Algerian Dialect Using Deep Learning: Conclusion, Acknowledgmentsby@escholar

Hate Speech Detection in Algerian Dialect Using Deep Learning: Conclusion, Acknowledgments

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In this paper, we proposed a complete end-to-end natural language processing (NLP) approach for hate speech detection in the Algerian dialect.
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This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.

Authors:

(1) Dihia LANASRI, OMDENA, New York, USA;

(2) Juan OLANO, OMDENA, New York, USA;

(3) Sifal KLIOUI, OMDENA, New York, USA;

(4) Sin Liang Lee, OMDENA, New York, USA;

(5) Lamia SEKKAI, OMDENA, New York, USA.

6 Conclusion

The importance of hate speech detection on social networks has encouraged many researchers to build solutions (corpora and classifiers) to detect suspect messages. The literature review shows that most works are interested in text in structured languages like English, French, Arabic, etc. However, few works deal with dialects, mainly the Algerian one, which is known for its complexity and variety. To fill in the gap, we propose in this paper a complete NLP approach to detect hate speech in the Algerian dialect. We built an annotated corpus of more than 13,5K documents, which is used to evaluate various deep learning architectures. The obtained results are very promising, where the most accurate was the DzaraShield .


Looking ahead, there is significant potential to enhance inference speed, particularly for the Dziribert-based and multilingual models. While this project primarily focused on Arabic characters, our next step will be to address the dialect when written in Latin characters. Embracing both Arabic and Latin characters will more accurately capture the nuances of the written Algerian dialect. Finally, we plan to expand our corpus size and explore alternative deep-learning architectures.

7 Acknowledgments

We would like to thank every person who has contributed to this project: Micha Freidin, Viktor Ivanenko, Piyush Aaryan, Yassine Elboustani, Tasneem Elyamany, Cephars Bonacci, Nolan Wang and Lydia Khelifa Chibout. We would also like to thank Omdena organization for giving us this valuable opportunity

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