Amira Hamed Abo-Elghit1,*, Taher Hamza1, Aya Al-Zoghby2
CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1967-1994, 2022, DOI:10.32604/cmc.2022.025353
Abstract Nowadays, we can use the multi-task learning approach to train a machine-learning algorithm to learn multiple related tasks instead of training it to solve a single task. In this work, we propose an algorithm for estimating textual similarity scores and then use these scores in multiple tasks such as text ranking, essay grading, and question answering systems. We used several vectorization schemes to represent the Arabic texts in the SemEval2017-task3-subtask-D dataset. The used schemes include lexical-based similarity features, frequency-based features, and pre-trained model-based features. Also, we used contextual-based embedding models such as Arabic Bidirectional Encoder Representations from Transformers (AraBERT). We… More >
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