论文标题
增强使用意图功能的插槽标记，用于使用Bert进行任务的自然语言理解
Enhancing Slot Tagging with Intent Features for Task Oriented Natural Language Understanding using BERT
论文作者
论文摘要
与单个模型相比，最近的联合意图检测和插槽标记模型的性能提高了。在许多实际数据集中，插槽标签和值与其意图标签具有很强的相关性。在这种情况下，意图标签信息可以充当插槽标记模型的有用功能。在本文中，我们通过3个技术在关节意图和插槽检测模型的插槽标记任务中使用3个技术来研究意图标签的效果。我们在基准口语数据集剪辑和ATI上以及大型私人Bixby数据集上评估了我们的技术，并观察到对最先进的模型的插槽标记性能的改进。
Recent joint intent detection and slot tagging models have seen improved performance when compared to individual models. In many real-world datasets, the slot labels and values have a strong correlation with their intent labels. In such cases, the intent label information may act as a useful feature to the slot tagging model. In this paper, we examine the effect of leveraging intent label features through 3 techniques in the slot tagging task of joint intent and slot detection models. We evaluate our techniques on benchmark spoken language datasets SNIPS and ATIS, as well as over a large private Bixby dataset and observe an improved slot-tagging performance over state-of-the-art models.
