End-to-End Dialog Neural Coreference Resolution: Balancing Efficiency and Accuracy in Large-Scale Systems
Journal:
arXiv
Published Date:
Apr 8, 2025
Abstract
Large-scale coreference resolution presents a significant challenge in
natural language processing, necessitating a balance between efficiency and
accuracy. In response to this challenge, we introduce an End-to-End Neural
Coreference Resolution system tailored for large-scale applications. Our system
efficiently identifies and resolves coreference links in text, ensuring minimal
computational overhead without compromising on performance. By utilizing
advanced neural network architectures, we incorporate various contextual
embeddings and attention mechanisms, which enhance the quality of predictions
for coreference pairs. Furthermore, we apply optimization strategies to
accelerate processing speeds, making the system suitable for real-world
deployment. Extensive evaluations conducted on benchmark datasets demonstrate
that our model achieves improved accuracy compared to existing approaches,
while effectively maintaining rapid inference times. Rigorous testing confirms
the ability of our system to deliver precise coreference resolutions
efficiently, thereby establishing a benchmark for future advancements in this
field.
