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Numer specjalny 3/2024 vol. 57
Statystyki
CC BY-SA 3.0
Pobierz cytowanie
PRACA POGLĄDOWA
Enhancing conversational ai with the Rasa framework: intent understanding and NLU pipeline optimization
Tomasz Smutek 1
,
 
Ewa Golec 1
,
 
Paweł Rymarczyk 2
,
 
Michał Jarmuł 1
,
 
Adam Hernas 3
 
 
 
Więcej
Ukryj
1
WSEI University
 
2
Netrix S.A.
 
3
Wyższa Szkoła Biznesu - National Louis University
 
 
Data nadesłania: 21-06-2024
 
 
Data akceptacji: 15-07-2024
 
 
Data publikacji: 20-08-2024
 
 
Autor do korespondencji
Ewa Golec   

WSEI University
 
 
JoMS 2024;57(Numer specjalny 3):531-548
DOI: https://doi.org/10.13166/jms/191223
 
 
Artykuł (PDF)
Referencje (20)
 
SŁOWA KLUCZOWE
Intent understanding
Rasa framework
Natural language processing
Artificial intelligence
Entity extraction
Intent classification
 
DZIEDZINY
Nauki o zarządzaniu
 
STRESZCZENIE
Implementing the Rasa NLU pipeline allowed intent detection and entity recognition, particularly in complex scenarios with multi-intent queries. Communication within the Rasa NLU pipeline was effectively managed, ensuring seamless data flow between components, which preserved context and enhanced interpretability. The voice assistant developed with STT and TTS capabilities demonstrated robust real-time natural language processing, handling spoken queries efficiently. This confirmed the practical viability of using the Rasa framework for scalable and customizable conversational AI applications. Discussing: The findings underscore the robustness of the Rasa NLU pipeline in handling diverse conversational demands and the flexibility of its components to adapt to different linguistic contexts. The research discusses the potential of integrating sophisticated NLU techniques to create more intuitive and responsive conversational agents, highlighting the critical role of context-aware processing in improving user interaction with AI systems.
 
REFERENCJE (20)
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