Deep learning is providing very positive results in areas related to conversational interfaces, such as speech recognition, but its potential benefit for dialog management has still not been fully studied. In this paper, we perform an assessment of different configurations for deep-learned dialog management with three dialog corpora from different application domains and varying in size, dimensionality and possible system responses. Our results have allowed us to identify several aspects that can have an impact on accuracy, including the approaches used for feature extraction, input representation, context consideration and the hyper-parameters of the deep neural networks employed.
@article{arxiv.2108.03478,
title = {An empirical assessment of deep learning approaches to task-oriented dialog management},
author = {Lukáš Matějů and David Griol and Zoraida Callejas and José Manuel Molina and Araceli Sanchis},
journal= {arXiv preprint arXiv:2108.03478},
year = {2021}
}