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 …

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 …

Abstract Recurrent Neural Network Language Models (RNN-LMs) have recently shown
exceptional performance across a variety of applications. In this paper, we modify the …

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

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 …

Recently, large pretrained language models have demonstrated strong language
understanding capabilities. This is particularly reflected in their zero-shot and in-context …

We present an approach to speech recognition that uses only a neural network to map
acoustic input to characters, a character-level language model, and a beam search …

Recurrent Neural Network (RNN) and one of its specific architectures, Long Short-Term
Memory (LSTM), have been widely used for sequence labeling. In this paper, we first …

Recurrent neural networks (RNNs) have recently produced record setting performance in
language modeling and word-labeling tasks. In the word-labeling task, the RNN is used …

Generative encoder-decoder models offer great promise in developing domain-general
dialog systems. However, they have mainly been applied to open-domain conversations …