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A Domain-Specific Intelligent Chatbot Using Rasa with Enhanced Natural Language Understanding
Subject area: Science,Engineering and Technology · Area of research: A.I and Natural Language Processing
DOI: https://doi.org/10.64388/IREV9I10-1716279
Abstract
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]
How to cite this paper
@article{1716279,
author = {P Bharat Chandra, Chinmaya Gouda, Kishan Kumar Bhuyan, Samraju Keshab, Swati Kanta Mishra},
title = {A Domain-Specific Intelligent Chatbot Using Rasa with Enhanced Natural Language Understanding},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {1131-1137},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716279.pdf},
abstract = {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]},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716279}
}