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Assessment of LSTM, ARABERT and Prompt-Based Learning for Gender Author Profiling in Modern Standard Arabic Language
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Assessment of LSTM, ARABERT and Prompt-Based Learning for Gender Author Profiling in Modern Standard Arabic Language
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Journal Article
Assessment of LSTM, ARABERT and Prompt-Based Learning for Gender Author Profiling in Modern Standard Arabic Language
2024
Overview
Author Profiling aims to extract persons’ characteristics (gender, age…) from their writings. This emerging field of NLP poses great challenges for all languages in general and, in particular, for the Modern Standard Arabic Language. This paper presents an assessment study of three state-of-the-art approaches used for gender author profiling, namely, LSTM, ARABERT, and Prompt-Based learning. Using a rich dataset created for this task, our research investigates the effectiveness of these methods in gender identification. Our findings indicate that the ARABERT method obtained the highest scores in terms of accuracy, ranging from 84.6% to 92.4%, and Prompt-Based learning performed competitively compared to ARABERT, with accuracy increasing from 84% to 92.3%. However, while LSTM also showed progress across all batches, it still consistently performed worse than the other two models and reached an accuracy of only 78.5%.
