@misc{10481/88561,
year = {2021},
url = {https://hdl.handle.net/10481/88561},
abstract = {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.},
organization = {This work was supported by the Student Grant Scheme 2020 of the Technical University in Liberec, the European Union’s Horizon 2020 research and innovation programme under grant agreement No 823907 (MENHIR project: https://menhir-project.eu), and by the Spanish project TEC2017-84593-C2-1-R.},
publisher = {Elsevier},
keywords = {Dialog management},
keywords = {Deep learning},
keywords = {Conversational systems},
title = {An empirical assessment of deep learning approaches  to task-oriented dialog management},
doi = {https://doi.org/10.1016/j.neucom.2020.01.126},
author = {Mateju, Lukas and Griol Barres, David and Callejas Carrión, Zoraida and Molina, José Manuel and Sanchis, Araceli},
}