Advancing Chatbot Conversations: A Review of Knowledge Update Approaches

Conversational systems like chatbots have emerged as powerful tools for automating interactive tasks traditionally confined to human involvement. Fundamental to chatbot functionality is their knowledge base, the foundation of their reasoning processes. A pivotal challenge resides in chatbots' i...

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Detalles Bibliográficos
Autores: da Costa, Luis Antonio L. F., Melchiades, Mateus Begnini, Girelli, Valéria Soldera, Colombelli, Felipe, Araújo, Denis Andrei de, Rigo, Sandro José, Ramos, Gabriel de Oliveira, da Costa, Cristiano André, Righi, Rodrigo da Rosa, Barbosa, Jorge Luis Victória
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:Brasil
Institución:Sociedade Brasileira de Computação (SBC)
Repositorio:Journal of the Brazilian Computer Society
Idioma:inglés
OAI Identifier:oai:journals-sol.sbc.org.br:article/2882
Acceso en línea:https://journals-sol.sbc.org.br/index.php/jbcs/article/view/2882
Access Level:acceso abierto
Palabra clave:Chatbots
Natural Language Processing
Artificial Intelligence
Data Extraction
Descripción
Sumario:Conversational systems like chatbots have emerged as powerful tools for automating interactive tasks traditionally confined to human involvement. Fundamental to chatbot functionality is their knowledge base, the foundation of their reasoning processes. A pivotal challenge resides in chatbots' innate incapacity to seamlessly integrate changes within their knowledge base, thereby hindering their ability to provide real-time responses. The increasing literature attention dedicated to effective knowledge base updates, which we term content update, underscores the significance of this topic. This work provides an overview of content update methodologies in the context of conversational agents. We delve into the state-of-the-art approaches for natural language understanding, such as language models and alike, which are essential for turning data into knowledge. Additionally, we discuss turning point strategies and primary resources, such as deep learning, which are crucial for supporting language models. As our principal contribution, we review and discuss the core techniques underpinning information extraction as well as knowledge base representation and update in the context of conversational agents.