Intent classification, to identify the speaker's intention, and slot filling, to label each token
with a semantic type, are critical tasks in natural language understanding. Traditionally the …

Deep learning methods possess many processing layers to understand the stratified
representation of data and have achieved state-of-art results in several domains. Recently …

Intent classification and slot filling are two essential tasks for natural language
understanding. They often suffer from small-scale human-labeled training data, resulting in …

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 …

One of the core components of modern spoken dialogue systems is the belief tracker, which
estimates the user's goal at every step of the dialogue. However, most current approaches …

Abstract Visual Question Answering (VQA) is a challenging task that has received increasing
attention from both the computer vision and the natural language processing communities …

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

The Internet of Things (IoT) has become quite popular due to advancements in Information
and Communications technologies and has revolutionized the entire research area in …

Abstract Spoken Language Understanding (SLU), which typically involves intent
determination and slot filling, is a core component of spoken dialogue systems. Joint …

Many natural language understanding (NLU) tasks, such as shallow parsing (ie, text
chunking) and semantic slot filling, require the assignment of representative labels to the …