Persian Pronoun Resolution: Leveraging Neural Networks and Language Models
<|reference_start|>Persian Pronoun Resolution: Leveraging Neural Networks and Language Models: 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.<|reference_end|>
@article{mohammadi2024persian,
    title={Persian Pronoun Resolution: Leveraging Neural Networks and Language
  Models},
    author={Hassan Haji Mohammadi, Alireza Talebpour, Ahmad Mahmoudi Aznaveh,
  Samaneh Yazdani},
    journal={arXiv preprint arXiv:2405.10714},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10714},
    primaryClass={cs.CL cs.AI}
}