A survey on recent advances and challenges in reinforcement learning methods for task-oriented dialogue policy learning

WC Kwan, HR Wang, HM Wang, KF Wong - Machine Intelligence …, 2023 - Springer
Dialogue policy learning (DPL) is a key component in a task-oriented dialogue (TOD)
system. Its goal is to decide the next action of the dialogue system, given the dialogue state …

Spoken language understanding using long short-term memory neural networks

K Yao, B Peng, Y Zhang, D Yu… - 2014 IEEE spoken …, 2014 - ieeexplore.ieee.org
Neural network based approaches have recently produced record-setting performances in
natural language understanding tasks such as word labeling. In the word labeling task, a …

[PDF][PDF] Recurrent neural networks for language understanding.

K Yao, G Zweig, MY Hwang, Y Shi, D Yu - Interspeech, 2013 - isca-archive.org
Abstract Recurrent Neural Network Language Models (RNN-LMs) have recently shown
exceptional performance across a variety of applications. In this paper, we modify the …

A self-attentive model with gate mechanism for spoken language understanding

C Li, L Li, J Qi - Proceedings of the 2018 conference on empirical …, 2018 - aclanthology.org
Abstract Spoken Language Understanding (SLU), which typically involves intent
determination and slot filling, is a core component of spoken dialogue systems. Joint …

[PDF][PDF] Pydial: A multi-domain statistical dialogue system toolkit

S Ultes, LMR Barahona, PH Su… - Proceedings of ACL …, 2017 - aclanthology.org
Abstract Statistical Spoken Dialogue Systems have been around for many years. However,
access to these systems has always been difficult as there is still no publicly available end-to …