Context-Aware AI Chatbot Using Transformer-Based Models for Intelligent User Interactions

Pooja S, Gokul G, Linkesh Mani K, Raj Kumar A S, Amutha Bharathi R

2025

Abstract

Chatbots have transformed user interaction with technology by offering immediate, automated support across multiple sectors. Advancements in artificial intelligence (AI) and natural language processing (NLP) are enhancing the efficiency and contextual awareness of contemporary chatbots. This study introduces an AI-driven chatbot system utilizing machine learning and natural language processing to facilitate effective and user-friendly conversations. The suggested chatbot utilizes sophisticated methodologies, including Transformer-based models like BERT for intent recognition and Named Entity Recognition (NER), as well as GPT for producing dynamic, human-like responses. The system is engineered to manage a variety of conversational duties, ranging from addressing frequently asked questions to executing transactional transactions. It incorporates preprocessing methods such as tokenization and text normalization to improve input comprehension and employs embeddings for contextual understanding. The chatbot employs conversational state tracking with recurrent neural networks (RNNs) or memory-augmented Transformers to facilitate coherent and contextually aware multi-turn discussions. Response generation employs a hybrid methodology, integrating template-driven answers for structured inquiries with dynamic replies for open-ended questions. The chatbot backend interfaces with REST APIs to retrieve external data, guaranteeing real-time capabilities for user-specific operations such as reservations or database inquiries. The system is implemented on scalable cloud platforms and is available through several channels, including online and mobile applications, as well as messaging networks such as WhatsApp and Telegram. Despite its efficacy, constraints encompass computational complexity for real-time Transformer-based inference and reliance on high-quality training data. Future improvements involve utilizing hybrid deep learning models to enhance scalability and robustness.

Paper Citation

in Harvard Style

S P., G G., Mani K L., A S R. and Bharathi R A. (2025). Context-Aware AI Chatbot Using Transformer-Based Models for Intelligent User Interactions. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 667-674. DOI: 10.5220/0013639800004664

in Bibtex Style

@conference{incoft25,
author={Pooja S and Gokul G and Linkesh Mani K and Raj Kumar A S and Amutha Bharathi R},
title={Context-Aware AI Chatbot Using Transformer-Based Models for Intelligent User Interactions},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={667-674},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013639800004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Context-Aware AI Chatbot Using Transformer-Based Models for Intelligent User Interactions
SN - 978-989-758-763-4
AU - S P.
AU - G G.
AU - Mani K L.
AU - A S R.
AU - Bharathi R A.
PY - 2025
SP - 667
EP - 674
DO - 10.5220/0013639800004664
PB - SciTePress