Persian Pronoun Resolution: Leveraging Neural Networks and Language Models
This addresses pronoun resolution for Persian language processing, which is an incremental advancement in a domain-specific area.
The study tackled Persian pronoun resolution by proposing the first end-to-end neural network system that jointly optimizes mention detection and antecedent linking, achieving a 3.37 F1 score improvement over the previous state-of-the-art on the Mehr corpus.
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.