Building a chatbot for the enterprise using transformer models and self-attention mechanisms

Authors

  • Sarbaree Mishra Program Manager at Molina Healthcare Inc., USA. Author

DOI:

https://doi.org/10.63282/3050-9262.IJAIDSML-V2I2P108

Keywords:

Chatbot, transformer models, self-attention mechanisms, NLP, enterprise AI, BERT, GPT, natural language understanding, conversational AI, machine learning, AI deployment, text preprocessing, tokenization, context management, fine-tuning, intent recognition, response generation, data privacy, model explainability, scalability, cloud-based solutions, loss function optimization, customer service automation, CRM integration, knowledge base, BLEU score, perplexity, API integration, data security

Abstract

As more and more businesses go digital, the demand for advanced conversational agents has never been higher. Chatbots are becoming a must-have for customer service, company communication & many more commercial uses. This study looks into transformer models, focusing on their self-attention processes, to make chatbots that are strong and can grow in size for use in industry. Transformers like BERT & GPT have altered how robots learn & use their human languages. Their self-attention method, which helps models figure out how these essential specific terms in a phrase are, is highly vital for improving their chatbots' ability to grasp what is going on there around them. Using these models, chatbots may have conversations that are more natural, accurate, & aware of the situation, which improves the user experience & the efficiency of the operation. This study looks at the basic structure of these transformer models, the training methods that make them work better for chatbots & the problems that companies run into when they try to use these systems in actual life. We also look at the practical reasons for adding more chatbot solutions to a business, such as keeping the models up to date, protecting their information, and making sure the systems work together. The report shows the best ways for businesses to deploy transformer-based chatbots, making sure that they meet the strict standards of reliability, performance & user satisfaction that companies need

References

[1] Saffar Mehrjardi, M. (2019). Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems.

[2] Yang, L., Qiu, M., Qu, C., Chen, C., Guo, J., Zhang, Y., ... & Chen, H. (2020, April). IART: Intent-aware response ranking with transformers in information-seeking conversation systems. In Proceedings of The Web Conference 2020 (pp. 2592-2598).

[3] Manda, Jeevan Kumar. "Securing Remote Work Environments in Telecom: Implementing Robust Cybersecurity Strategies to Secure Remote Workforce Environments in Telecom, Focusing on Data Protection and Secure Access Mechanisms." Focusing on Data Protection and Secure Access Mechanisms (April 04, 2020) (2020).

[4] Iosifova, O., Iosifov, I., Rolik, O., & Sokolov, V. Y. (2020). Techniques comparison for natural language processing. MoMLeT&DS, 2631(I), 57-67.

[5] Immaneni, J. (2020). Using Swarm Intelligence and Graph Databases Together for Advanced Fraud Detection. Journal of Big Data and Smart Systems, 1(1).

[6] Yu, C., Jiang, W., Zhu, D., & Li, R. (2019, November). Stacked multi-head attention for multi-turn response selection in retrieval-based chatbots. In 2019 Chinese Automation Congress (CAC) (pp. 3918-3921). IEEE.

[7] Nookala, Guruprasad. "End-to-End Encryption in Data Lakes: Ensuring Security and Compliance." Journal of Computing and Information Technology 1.1 (2021).

[8] Mohammad, Abdul Jabbar. “Sentiment-Driven Scheduling Optimizer”. International Journal of Emerging Research in Engineering and Technology, vol. 1, no. 2, June 2020, pp. 50-59

[9] Su, T. C., & Chen, G. Y. (2019). ET-USB: Transformer-Based Sequential Behavior Modeling for Inbound Customer Service. arXiv preprint arXiv:1912.10852.

[10] Immaneni, J., & Salamkar, M. (2020). Cloud migration for fintech: how kubernetes enables multi-cloud success. International Journal of Emerging Trends in Computer Science and Information Technology, 1(3), 17-28.

[11] Talakola, Swetha. “Comprehensive Testing Procedures”. International Journal of AI, BigData, Computational and Management Studies, vol. 2, no. 1, Mar. 2021, pp. 36-46

[12] Singla, S., & Ramachandra, N. (2020). Comparative analysis of transformer based pre-trained NLP Models. Int. J. Comput. Sci. Eng, 8, 40-44.

[13] Shaik, Babulal. "Automating Compliance in Amazon EKS Clusters With Custom Policies." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 587-10.

[14] Arugula, Balkishan, and Sudhkar Gade. “Cross-Border Banking Technology Integration: Overcoming Regulatory and Technical Challenges”. International Journal of Emerging Research in Engineering and Technology, vol. 1, no. 1, Mar. 2020, pp. 40-48

[15] Chen, J., Agbodike, O., & Wang, L. (2020). Memory-based deep neural attention (mDNA) for cognitive multi-turn response retrieval in task-oriented chatbots. Applied Sciences, 10(17), 5819.

[16] Mohammad, Abdul Jabbar, and Waheed Mohammad A. Hadi. “Time-Bounded Knowledge Drift Tracker”. International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 2, no. 2, June 2021, pp. 62-71

[17] Nookala, G. (2020). Automation of privileged access control as part of enterprise control procedure. Journal of Big Data and Smart Systems, 1(1).

[18] Liu, C., Jiang, J., Xiong, C., Yang, Y., & Ye, J. (2020, August). Towards building an intelligent chatbot for customer service: Learning to respond at the appropriate time. In Proceedings of the 26th ACM SIGKDD international conference on Knowledge Discovery & Data Mining (pp. 3377-3385).

[19] Manda, Jeevan Kumar. "Cloud Security Best Practices for Telecom Providers: Developing comprehensive cloud security frameworks and best practices for telecom service delivery and operations, drawing on your cloud security expertise." Available at SSRN 5003526 (2020).

[20] Zhao, H., Lu, J., & Cao, J. (2020). A short text conversation generation model combining BERT and context attention mechanism. International Journal of Computational Science and Engineering, 23(2), 136-144.

[21] Veluru, Sai Prasad. "Leveraging AI and ML for Automated Incident Resolution in Cloud Infrastructure." International Journal of Artificial Intelligence, Data Science, and Machine Learning 2.2 (2021): 51-61.

[22] Shaik, Babulal. "Network Isolation Techniques in Multi-Tenant EKS Clusters." Distributed Learning and Broad Applications in Scientific Research 6 (2020).

[23] Cai, Y., Zuo, M., Zhang, Q., Xiong, H., & Li, K. (2020). A Bichannel Transformer with Context Encoding for Document‐Driven Conversation Generation in Social Media. Complexity, 2020(1), 3710104.

[24] Patel, Piyushkumar. "The Role of Financial Stress Testing During the COVID-19 Crisis: How Banks Ensured Compliance With Basel III." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 789-05.

[25] Damani, S., Narahari, K. N., Chatterjee, A., Gupta, M., & Agrawal, P. (2020, May). Optimized transformer models for faq answering. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 235-248). Cham: Springer International Publishing.

[26] Sai Prasad Veluru. “Hybrid Cloud-Edge Data Pipelines: Balancing Latency, Cost, and Scalability for AI”. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), vol. 7, no. 2, Aug. 2019, pp. 109–125

[27] Manda, J. K. "Implementing blockchain technology to enhance transparency and security in telecom billing processes and fraud prevention mechanisms, reflecting your blockchain and telecom industry insights." Adv Comput Sci 1.1 (2018).

[28] Jani, Parth. "Privacy-Preserving AI in Provider Portals: Leveraging Federated Learning in Compliance with HIPAA." The Distributed Learning and Broad Applications in Scientific Research 6 (2020): 1116-1145.

[29] Heidari, M., & Rafatirad, S. (2020, December). Semantic convolutional neural network model for safe business investment by using bert. In 2020 Seventh International Conference on social networks analysis, management and security (SNAMS) (pp. 1-6). IEEE.

[30] Patel, Piyushkumar. "Transfer Pricing in a Post-COVID World: Balancing Compliance With New Global Tax Regimes." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 208-26

[31] Arugula, Balkishan. “Change Management in IT: Navigating Organizational Transformation across Continents”. International Journal of AI, BigData, Computational and Management Studies, vol. 2, no. 1, Mar. 2021, pp. 47-56

[32] Immaneni, J. (2021). Securing Fintech with DevSecOps: Scaling DevOps with Compliance in Mind. Journal of Big Data and Smart Systems, 2.

[33] Emmerich, M., Lytvyn, V., Vysotska, V., Basto-Fernandes, V., & Lytvynenko, V. (2020). Modern Machine Learning Technologies and Data Science Workshop.

[34] Jani, Parth. "UM Decision Automation Using PEGA and Machine Learning for Preauthorization Claims." The Distributed Learning and Broad Applications in Scientific Research 6 (2020): 1177-1205.

[35] Csaky, R. (2019). Deep learning based chatbot models. arXiv preprint arXiv:1908.08835.

[36] Liu, R., Chen, M., Liu, H., Shen, L., Song, Y., & He, X. (2020). Enhancing multi-turn dialogue modeling with intent information for E-commerce customer service. In Natural Language Processing and Chinese Computing: 9th CCF International Conference, NLPCC 2020, Zhengzhou, China, October 14–18, 2020, Proceedings, Part I 9 (pp. 65-77). Springer International Publishing.

[37] Sreejith Sreekandan Nair, Govindarajan Lakshmikanthan (2020). Beyond VPNs: Advanced Security Strategies for the Remote Work Revolution

Published

2021-06-30

Issue

Section

Articles

How to Cite

1.
Mishra S. Building a chatbot for the enterprise using transformer models and self-attention mechanisms. IJAIDSML [Internet]. 2021 Jun. 30 [cited 2025 Oct. 10];2(2):72-8. Available from: https://ijaidsml.org/index.php/ijaidsml/article/view/216