Multi-Purpose NLP Chatbot : Design, Methodology & Conclusion

• URL: http://arxiv.org/abs/2310.08977v1
• Date: Fri, 13 Oct 2023 09:47:24 GMT
• Title: Multi-Purpose NLP Chatbot : Design, Methodology & Conclusion
• Authors: Shivom Aggarwal, Shourya Mehra, Pritha Mitra
• Abstract summary: This research paper provides a thorough analysis of the chatbots technology environment as it exists today. It provides a very flexible system that makes use of reinforcement learning strategies to improve user interactions and conversational experiences. The complexity of chatbots technology development is also explored in this study, along with the causes that have propelled these developments and their far-reaching effects on a range of sectors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With a major focus on its history, difficulties, and promise, this research paper provides a thorough analysis of the chatbot technology environment as it exists today. It provides a very flexible chatbot system that makes use of reinforcement learning strategies to improve user interactions and conversational experiences. Additionally, this system makes use of sentiment analysis and natural language processing to determine user moods. The chatbot is a valuable tool across many fields thanks to its amazing characteristics, which include voice-to-voice conversation, multilingual support [12], advising skills, offline functioning, and quick help features. The complexity of chatbot technology development is also explored in this study, along with the causes that have propelled these developments and their far-reaching effects on a range of sectors. According to the study, three crucial elements are crucial: 1) Even without explicit profile information, the chatbot system is built to adeptly understand unique consumer preferences and fluctuating satisfaction levels. With the use of this capacity, user interactions are made to meet their wants and preferences. 2) Using a complex method that interlaces Multiview voice chat information, the chatbot may precisely simulate users' actual experiences. This aids in developing more genuine and interesting discussions. 3) The study presents an original method for improving the black-box deep learning models' capacity for prediction. This improvement is made possible by introducing dynamic satisfaction measurements that are theory-driven, which leads to more precise forecasts of consumer reaction.

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