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An accurate intent recognition model, together with precise slot extraction and a well-formed response generator, is key to building efficient task-oriented dialogue systems in practice. This paper introduces a fully-automated end-to-end dialogue system that leverages two interchangeable classification models (a classical TF-IDF pipeline, which supports SVM, Na¨ıve Bayes and Logistic Regression algorithms, and a DistilBERT fine-tuned classifier based on transformers) with a lightweight rule-based slot extractor and a simple response generator driven by templates. The proposed dialogue system relies on the pipeline of generating and splitting a structured dataset (generate_dataset.py) from a hand-curated intents corpus (intents_full.csv) and evaluates the performance using a dedicated unified evaluator reporting metrics including per-intent accuracy, precision, recall, F1-score and confusion matrix. Finally, a web-based Gradio application (app.py) enables inter-active testing via submitting textual queries to predict the intents’ classes, the corresponding slot values and templated responses. Results show that DistilBERT outperforms the other approaches on ambiguous intents classification whereas the TF-IDF pipeline proves to be considerably faster in terms of inference latency.
"A Task-Oriented NLP Chatbot System with Dual-Backend Intent Classification, Slot Extraction, and Template Response Generation", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.a314-a319, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605036.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator