This tutorial surveys neural approaches to conversational AI that were developed in the last
few years. We group conversational systems into three categories:(1) question answering …

Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …

There is a recent interest in investigating few-shot NER, where the low-resource target
domain has different label sets compared with a resource-rich source domain. Existing …

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 recurrent neural network models for joint intent detection and slot filling
have achieved the state-of-the-art performance, while they have independent attention …

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a
better understanding of the human language for linguistic-based human-computer …

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 …

Nowadays it is the era of intelligent machine. With the advancement of artificial intelligent,
machine learning and deep learning, machines have started to impersonate as human …

One of the major drawbacks of modularized task-completion dialogue systems is that each
module is trained individually, which presents several challenges. For example, downstream …

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 …