Dialog state tracking (DST), which estimates dialog states given a dialog context, is a core component in task-oriented dialog systems. Existing data-driven methods usually extract features automatically through deep learning. However, most of these models have limitations. First, compared with hand-crafted delexicalization features, such features in deep learning approaches are not universal. However, they are important for tracking unseen slot values. Second, such models do not work well in situations where noisy labels are ubiquitous in datasets. To address these challenges, we propose a robust dialog state tracker with contextual-feature augmentation. Contextual-feature augmentation is used to extract generalized features; hence, it is capable of solving the unseen slot value tracking problem. We apply a simple but effective deep learning paradigm to train our DST model with noisy labels. The experimental results show that our model achieves state-of-the-art scores in terms of joint accuracy on the MultiWOZ 2.0 dataset. In addition, we show its performance in tracking unseen slot values by simulating unseen domain dialog state tracking.

对话框状态跟踪（DST）是在面向任务的对话框系统中的核心组件，它在给定对话框上下文的情况下估计对话框状态。现有的数据驱动方法通常通过深度学习自动提取特征。但是，这些模型大多数都有局限性。首先，与手工进行的去词法化功能相比，深度学习方法中的此类功能并不普遍。但是，它们对于跟踪看不见的插槽值很重要。其次，这种模型在嘈杂的标签在数据集中无处不在的情况下效果不佳。为了解决这些挑战，我们提出了一个具有上下文特征增强功能的健壮的对话框状态跟踪器。上下文特征增强用于提取广义特征。因此，它能够解决看不见的时隙值跟踪问题。我们应用简单但有效的深度学习范例来训练带有噪音标签的DST模型。实验结果表明，在MultiWOZ 2.0数据集上，我们的模型在联合精度方面达到了最新的分数。此外，我们通过模拟看不见的域对话框状态跟踪来显示其在跟踪看不见的插槽值方面的性能。