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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neurocomputing

ISSN

0925-2312

e-ISSN

—

Svazek periodika

439

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

327-339

Kód UT WoS článku

000642585600011

EID výsledku v databázi Scopus

2-s2.0-85101537442