The latency in the current neural based dialogue state tracking models prohibits them from being used efficiently for deployment in production systems, albeit their highly accurate performance. This paper proposes a new scalable and accurate neural dialogue state tracking model, based on the recently proposed Global-Local Self-Attention encoder (GLAD) model by Zhong et al. which uses global modules to share parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. By using only one recurrent networks with global conditioning, compared to (1 + \# slots) recurrent networks with global and local conditioning used in the GLAD model, our proposed model reduces the latency in training and inference times by $35\%$ on average, while preserving performance of belief state tracking, by $97.38\%$ on turn request and $88.51\%$ on joint goal and accuracy. Evaluation on Multi-domain dataset (Multi-WoZ) also demonstrates that our model outperforms GLAD on turn inform and joint goal accuracy.

本文提出了一种新的可伸缩且准确的神经对话状态跟踪模型，基于最近由Zhong等人提出的全局-本地自注意编码器（GLAD）模型，该模型使用全局模块共享不同类型（称为slots）对话状态估计器之间的参数，并使用局部模块学习slot-specific特征，相较于GLAD模型的全局和局部调节使用的（1+ # slots）个循环网络，我们提出的模型仅使用一个循环网络进行全局调节，平均而言在训练和推断时间方面可以减少35%的延迟，同时保持置信状态跟踪的表现，转向请求率为97.38％，联合目标和准确性为88.51％，在多领域数据集（Multi-WoZ）上的评估也表明，我们的模型在转向信息和联合目标精度上优于GLAD。

面向可扩展的神经对话状态追踪模型