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 …

A key notion in numerous data mining and machine learning (ML) algorithms says that the
training data and testing data are essentially in the similar feature space and also have the …

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 …

Dialogue systems are becoming an ubiquitous presence in our everyday lives having a
huge impact on business and society. Spoken language understanding (SLU) is the critical …

Task-oriented dialog systems increasingly rely on deep learning-based slot filling models,
usually needing extensive labeled training data for target domains. Often, however, little to …

Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot
filling, which are generally modeled jointly in existing works. However, most existing models …

We propose a generative framework for joint sequence labeling and sentence-level
classification. Our model performs multiple sequence labeling tasks at once using a single …

Dialog act prediction is an essential language comprehension task for both dialog system
building and discourse analysis. Previous dialog act schemes, such as SWBD-DAMSL, are …

Abstract Internet of Things (IoT) based voice interaction system, as a new artificial
intelligence application, provides a new human–computer interaction mode. The more …

Intent-detection (ID) and slot-filling (SF) are fundamental tasks for natural language
understanding. This study introduces a new encoder–decoder CNN-Transformer-based …