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 …

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 …

General-purpose language models have demonstrated impressive capabilities, performing
on par with state-of-the-art approaches on a range of downstream natural language …

The large pre-trained BERT has achieved remarkable performance on Natural Language
Processing (NLP) tasks but is also computation and memory expensive. As one of the …

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 …

In recent years, fostered by deep learning technologies and by the high demand for
conversational AI, various approaches have been proposed that address the capacity to …

Open intent classification is a challenging task in dialogue systems. On the one hand, it
should ensure the quality of known intent identification. On the other hand, it needs to detect …

Discovering new intents is a crucial task in dialogue systems. Most existing methods are
limited in transferring the prior knowledge from known intents to new intents. These methods …

Natural language understanding (NLU) in the context of goal-oriented dialog systems
typically includes intent classification and slot labeling tasks. Existing methods to expand an …

Spoken Language Understanding (SLU) aims to extract the semantics frame of user queries,
which is a core component in a task-oriented dialog system. With the burst of deep neural …