This paper presents the design, implementation, and evaluation of a domain-specific intelligent chatbot built using the Rasa open-source framework, augmented with enhanced Natural Language Understanding (NLU) techniques. Modern conversational agents face significant challenges in accurately interpreting domain-constrained user queries — from intent ambiguity to multi-entity extraction in complex utterances. This work leverages Rasa's Dual Intent and Entity Transformer (DIET) classifier 10 alongside the Transformer Embedding Dialogue (TED) Policy to achieve high-performance intent classification and contextual dialogue management. The proposed system was evaluated on a healthcare/customer support domain dataset, achieving an intent detection accuracy of 92.1% and an entity recognition F1-score of 0.88. Experimental results validate the superiority of the context-aware pipeline over single-turn baseline NLU models, demonstrating a 25% improvement in human-centric evaluation metrics. The system demonstrates that combining Rasa's modular NLU pipeline with transformer-based embeddings enables scalable, interpretable, and accurate domain-specific conversational AI.6
Building similarity graph...
Analyzing shared references across papers
Loading...
Chandra et al. (Mon,) studied this question.
www.synapsesocial.com/papers/69df2c50e4eeef8a2a6b15d4 — DOI: https://doi.org/10.64388/irev9i10-1716279
P Bharat Chandra
Chinmaya Gouda
Kishan Kumar Bhuyan
Building similarity graph...
Analyzing shared references across papers
Loading...