Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/129560
Título: Improving Dialogue Management Through Data Optimization
Autores/as: Medina Ramírez, Miguel Ángel 
Guerra Artal, Cayetano 
Hernández Tejera, Francisco Mario 
Clasificación UNESCO: 330405 Sistemas de reconocimiento de caracteres
Palabras clave: Dialog Systems
Dialogue management
Dataset quality
Supervised learning
Fecha de publicación: 2024
Publicación seriada: International Journal on Natural Language Computing 
Resumen: In task-oriented dialogue systems, the ability for users to effortlessly communicate with machines and computers through natural language stands as a critical advancement. Central to these systems is the dialogue manager, a pivotal component tasked with navigating the conversation to effectively meet user goals by selecting the most appropriate response. Traditionally, the development of sophisticated dialogue management has embraced a variety of methodologies, including rule-based systems, reinforcement learning, and supervised learning, all aimed at optimizing response selection in light of user inputs. This research casts a spotlight on the pivotal role of data quality in enhancing the performance of dialogue managers. Through a detailed examination of prevalent errors within acclaimed datasets, such as Multiwoz 2.1 and SGD, we introduce an innovative synthetic dialogue generator designed to control the introduction of errors precisely. Our comprehensive analysis underscores the critical impact of dataset imperfections, especially mislabeling, on the challenges inherent in refining dialogue management processes.
URI: http://hdl.handle.net/10553/129560
ISSN: 2319-4111
DOI: 10.5121/ijnlc.2024.13105
Fuente: International Journal on Natural Language Computing (IJNLC) [ISSN 2319-4111], v. 13 (1), p. 71-88
Colección:Artículos
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