@misc{10481/80256,
year = {2022},
url = {https://hdl.handle.net/10481/80256},
abstract = {Dialogue systems have an increasingly higher number of applications and so their development
is raising interest both in academic and industrial setting. Dialogue management is a key aspect
for the development of these systems, as it is in charge of the decision making processes and the
identification of the most appropriate responses to the user inputs. In this paper we compare different
statistical techniques to train dialogue managers using generally available corpora at the disposal
for the scientific community. Our results show that the use of generative models and in particular
an intent classifier with Neural Networks and Seq2Seq using GRU cells attain the best accuracy for
dialogue management.},
publisher = {Springer},
keywords = {Dialogue systems},
keywords = {Dialogue management},
keywords = {Statistical techniques},
keywords = {Deep learning},
title = {Comparison of Learning Approaches to Dialogue Management in Conversational Systems},
author = {Griol Barres, David and Callejas Carrión, Zoraida},
}