Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/128999
Título: Analysis of the impact of dataset quality on task-oriented dialogue management
Autores/as: Medina Ramírez, Miguel Ángel 
Guerra Artal, Cayetano 
Hernández Tejera, Mario 
Clasificación UNESCO: 120304 Inteligencia artificial
Palabras clave: Dialog systems
Dialogue management
Dataset quality
Supervised learning
Fecha de publicación: 2024
Conferencia: 10th International Conference on Natural Language Processing (NATP 2024) 
Resumen: Task-oriented dialogue systems (TODS) have become crucial for users to interact with machines and computers using natural language. One of its key com- ponents is the dialogue manager, which guides the conversation towards a good goal for the user by providing the best possible response. Previous works have proposed rule-based systems (RBS), reinforcement learning (RL), and supervised learning (SL) as solutions for the correct dialogue management; in other words, select the best response given input by the user. This work explores the impact of dataset quality on the performance of dialogue managers. We delve into po- tential errors in popular datasets, such as Multiwoz 2.1 and SGD. For our inves- tigation, we developed a synthetic dialogue generator to regulate the type and magnitude of errors introduced. Our findings suggest that dataset inaccuracies, like mislabeling, might play a significant role in the challenges faced in dialogue management.
URI: http://hdl.handle.net/10553/128999
ISBN: 978-1-923107-18-2
DOI: 10.5121/csit.2024.140420
Fuente: 10th International Conference on Natural Language Processing (NATP 2024) February 24 ~ 25, 2024, Vancouver, Canada
URL: https://acsty2024.org/natp/papers
Colección:Actas de congresos
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