Automatic Difficulty Classification of Arabic Sentences
This work addresses the need for automated difficulty assessment in Arabic language learning, though it is incremental as it applies existing embedding methods to a specific domain.
The paper tackles the problem of automatically classifying the difficulty of Arabic sentences for language learners, achieving an F-1 score of 0.80 for 3-way CEFR classification and 0.94 for binary classification using fine-tuned Arabic-BERT.
In this paper, we present a Modern Standard Arabic (MSA) Sentence difficulty classifier, which predicts the difficulty of sentences for language learners using either the CEFR proficiency levels or the binary classification as simple or complex. We compare the use of sentence embeddings of different kinds (fastText, mBERT , XLM-R and Arabic-BERT), as well as traditional language features such as POS tags, dependency trees, readability scores and frequency lists for language learners. Our best results have been achieved using fined-tuned Arabic-BERT. The accuracy of our 3-way CEFR classification is F-1 of 0.80 and 0.75 for Arabic-Bert and XLM-R classification respectively and 0.71 Spearman correlation for regression. Our binary difficulty classifier reaches F-1 0.94 and F-1 0.98 for sentence-pair semantic similarity classifier.