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。