An Empirical Assessment of Deep Learning Approaches to Task-Oriented Dialog Management

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F21%3A00009301" target="_blank" >RIV/46747885:24220/21:00009301 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0925231221001466" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0925231221001466</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neucom.2020.01.126" target="_blank" >10.1016/j.neucom.2020.01.126</a>

Result language
angličtina
Original language name
An Empirical Assessment of Deep Learning Approaches to Task-Oriented Dialog Management
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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