端到端神经共指消解系统平衡效率与精度，适合大规模应用。
End-to-End Dialog Neural Coreference Resolution: Balancing Efficiency and Accuracy in Large-Scale Systems
- 采用先进神经网络架构融合上下文嵌入与注意力机制
- 在基准数据集上准确率优于现有方法，推理速度快
- 优化策略提升处理效率，适合真实场景部署
大规模共指消解在自然语言处理中面临效率与精度的平衡挑战。为此，我们提出一种面向大规模应用的端到端神经共指消解系统。该系统通过先进的神经网络架构，融合多种上下文嵌入与注意力机制，显著提升共指对预测质量。同时，引入优化策略以加速处理速度，实现低计算开销下的高性能表现。在多个基准数据集上的广泛评估表明，该模型在准确率上优于现有方法，且保持快速推理时间。严格测试证实系统能高效、精确地完成共指消解任务，为该领域未来发展树立了新基准。
原文摘要 · Abstract (English)
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.
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