Cloud/edge computing and deep learning greatly improve performance of semantic understanding systems, where cloud/edge computing provides flexible, pervasive computation and storage capabilities to support variant applications, and deep learning models could comprehend text inputs by consuming computing and storage resource. Therefore, we propose to implement an intelligent online custom service system with power of both technologies. Essentially, task of semantic understanding consists of two subtasks, i.e., intent recognition and slot filling. To prevent error accumulation caused by modeling two subtasks independently, we propose to jointly model both subtasks in an end-to-end neural network. Specifically, the proposed method firstly extracts distinctive features with a dual structure to take full advantage of interactive and level information between two sub-tasks. Afterwards, we introduce attention scheme to enhance feature representation by involving sentence-level context information. With the support of cloud/edge computing infrastructure, we deploy the proposed network to work as an intelligent dialogue system for electrical customer service. During experiments, we test the proposed method and several comparative studies on public ATIS and our collected PSCF dataset. Experiment results prove the effectiveness of the proposed method by obtaining accurate and promising results.

云/边缘计算和深度学习极大地提高了语义理解系统的性能，其中云/边缘计算提供了灵活、普适的计算和存储能力来支持变体应用，并且深度学习模型可以通过消耗计算和存储资源来理解文本输入。因此，我们建议实施一个兼具两种技术力量的智能在线定制服务系统。本质上，语义理解任务由两个子任务组成，即意图识别和槽填充。为了防止独立建模两个子任务导致的错误累积，我们建议在端到端神经网络中联合建模两个子任务。具体来说，所提出的方法首先提取具有双重结构的显着特征，以充分利用两个子任务之间的交互和级别信息。之后，我们引入了注意力机制，通过涉及句子级上下文信息来增强特征表示。在云/边缘计算基础设施的支持下，我们将提议的网络部署为电气客户服务的智能对话系统。在实验期间，我们测试了所提出的方法以及对公共 ATIS 和我们收集的 PSCF 数据集的一些比较研究。实验结果通过获得准确和有希望的结果证明了所提出方法的有效性。在云/边缘计算基础设施的支持下，我们将提议的网络部署为电气客户服务的智能对话系统。在实验期间，我们测试了所提出的方法以及对公共 ATIS 和我们收集的 PSCF 数据集的一些比较研究。实验结果通过获得准确和有希望的结果证明了所提出方法的有效性。在云/边缘计算基础设施的支持下，我们将提议的网络部署为电气客户服务的智能对话系统。在实验期间，我们测试了所提出的方法以及对公共 ATIS 和我们收集的 PSCF 数据集的一些比较研究。实验结果通过获得准确和有希望的结果证明了所提出方法的有效性。