Abstract

Internet of Things (IoT) based voice interaction system, as a new artificial intelligence application, provides a new human–computer interaction mode. The more intelligent and efficient communication approach poses greater challenges to the semantic understanding module in the system. Facing with the complex and diverse interactive scenarios in practical applications, the academia and the industry urgently need more powerful Natural Language Understanding (NLU) methods as support. Intent Detection and Slot Filling joint task, as one of the core sub-tasks in NLU, has been widely used in different human–computer interaction scenarios. In the current era of deep learning, the joint task of Intent Detection and Slot Filling has also changed from previous rule-based methods to deep learning-based methods. It is an important problem to explore how to realize the models of these tasks to be refined and targeted designed, and to make the Intent Detection task better serve the improvement of precision of Slot Filling task by connecting the before and after tasks. It has great significance for building a more humanized IoT voice interaction system. In this study, we designed two joint models to realize Intent Detection and Slot Filling joint task. For the Intent Detection type task, one is based on BiGRU-Att-CapsuleNet (hybrid-based model) and the other is based on the RCNN model. Both methods use the BiGRU-CRF model for the Slot Filling type task. The hybrid-based model can enhance the semantic capture capability of a single model. And by combining specialized models built independently for each task to achieve a complete joint task, it can be better to achieve optimal performance on each task. This study also carried out detailed comparative experiments of tasks and joint tasks on multiple datasets. Experiments show that the joint models have achieved competitive results in 7 typical datasets included in multiple scenarios in English and Chinese compared with other models.

中文翻译：

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基于意图检测和时隙填充的联合任务的物联网语音交互自然语言理解方法

摘要

基于物联网（IoT）的语音交互系统作为一种新的人工智能应用程序，提供了一种新的人机交互模式。更智能，更有效的通信方法给系统中的语义理解模块带来了更大的挑战。面对实际应用中复杂多样的交互场景，学术界和业界迫切需要更强大的自然语言理解（NLU）方法作为支持。作为NLU的核心子任务之一，意图检测和插槽填充联合任务已广泛用于不同的人机交互场景中。在当前的深度学习时代，意图检测和插槽填充的共同任务也已从以前的基于规则的方法变为基于深度学习的方法。探索如何实现这些任务的模型进行细化和针对性设计，并通过连接前后任务，使意图检测任务更好地服务于插槽填充任务的精度提高，是一个重要的问题。这对于构建更加人性化的物联网语音交互系统具有重要意义。在这项研究中，我们设计了两个联合模型来实现意图检测和插槽填充联合任务。对于“意图检测”类型任务，一个基于BiGRU-Att-CapsuleNet（基于混合的模型），另一个基于RCNN模型。两种方法都将BiGRU-CRF模型用于插槽填充类型任务。基于混合的模型可以增强单个模型的语义捕获能力。通过结合为每个任务独立构建的专业模型来完成一个完整的联合任务，在每个任务上实现最佳性能可能会更好。这项研究还对多个数据集上的任务和联合任务进行了详细的比较实验。实验表明，与其他模型相比，联合模型在包括英语和汉语的多种场景中的7个典型数据集中取得了竞争性结果。