An empirical assessment of deep learning approaches to task-oriented dialog management Mateju, Lukas Griol Barres, David Callejas Carrión, Zoraida Molina, José Manuel Sanchis, Araceli Dialog management Deep learning Conversational systems 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. 2024-02-07T11:01:59Z 2024-02-07T11:01:59Z 2021 info:eu-repo/semantics/article https://hdl.handle.net/10481/88561 https://doi.org/10.1016/j.neucom.2020.01.126 eng info:eu-repo/grantAgreement/EC/H2020/875329 http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier