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.

该研究提出了一种用于波斯语代词消解的端到端神经网络系统，利用预训练的Transformer模型如ParsBERT。我们的系统同时优化了提及检测和先行链路，相比前期依赖基于规则和统计方法的最新系统，在Mehr语料库上实现了3.37 F1分数的提高。这一显著改进展示了将神经网络与语言模型结合的有效性，可能标志着波斯语代词消解领域的显著进展，并为进一步的研究铺平了道路。

波斯代词消解：利用神经网络和语言模型