  Coreference resolution, critical for identifying textual entities referencing
the same entity, faces challenges in pronoun resolution, particularly
identifying pronoun antecedents. Existing methods often treat pronoun
resolution as a separate task from mention detection, potentially missing
valuable information. This study proposes the first end-to-end neural network
system for Persian pronoun resolution, leveraging pre-trained Transformer
models like ParsBERT. Our system jointly optimizes both mention detection and
antecedent linking, achieving a 3.37 F1 score improvement over the previous
state-of-the-art system (which relied on rule-based and statistical methods) on
the Mehr corpus. This significant improvement demonstrates the effectiveness of
combining neural networks with linguistic models, potentially marking a
significant advancement in Persian pronoun resolution and paving the way for
further research in this under-explored area.
