Machine learning is widely used to solve networking challenges, ranging from traffic
classification and anomaly detection to network configuration. However, machine learning …

In the field of artificial intelligence, human–computer interaction (HCI) technology and its
related intelligent robot technologies are essential and interesting contents of research …

Attention-based encoder-decoder neural network models have recently shown promising
results in machine translation and speech recognition. In this work, we propose an attention …

Semantic slot filling is one of the most challenging problems in spoken language
understanding (SLU). In this paper, we propose to use recurrent neural networks (RNNs) for …

Intent detection and slot filling are two main tasks for building a spoken language
understanding (SLU) system. The two tasks are closely tied and the slots often highly …

Sequence-to-sequence deep learning has recently emerged as a new paradigm in
supervised learning for spoken language understanding. However, most of the previous …

Applications of Deep Belief Nets (DBN) to various problems have been the subject of a
number of recent studies ranging from image classification and speech recognition to audio …

Two major tasks in spoken language understanding (SLU) are intent determination (ID) and
slot filling (SF). Recurrent neural networks (RNNs) have been proved effective in SF, while …

Intent detection and slot filling are two main tasks for building a spoken language
understanding (SLU) system. The two tasks are closely related and the information of one …

Intent detection and slot filling are two main tasks for building a spoken language
understanding (SLU) system. Multiple deep learning based models have demonstrated …