Persian Pronoun Resolution: Leveraging Neural Networks and Language Models

• URL: http://arxiv.org/abs/2405.10714v1
• Date: Fri, 17 May 2024 11:56:00 GMT
• Title: Persian Pronoun Resolution: Leveraging Neural Networks and Language Models
• Authors: Hassan Haji Mohammadi, Alireza Talebpour, Ahmad Mahmoudi Aznaveh, Samaneh Yazdani,
• Abstract summary: 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 optimize both mention detection and antecedent linking, achieving a 3.37 F1 score improvement over the previous state-of-the-art system.
• Score: 8.604145658574689
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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