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Ezafe is a grammatical particle in some Iranian languages that links twowords together. Regardless of the important information it conveys, it isalmost always not indicated in Persian script, resulting in mistakes in readingcomplex sentences and errors in natural language processing tasks. In thispaper, we experiment with different machine learning methods to achievestate-of-the-art results in the task of ezafe recognition. Transformer-basedmethods, BERT and XLMRoBERTa, achieve the best results, the latter achieving2.68% F1-score more than the previous state-of-the-art. We, moreover, use ezafeinformation to improve Persian part-of-speech tagging results and show thatsuch information will not be useful to transformer-based methods and explainwhy that might be the case.

Persian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging