Jewelry Shop Conversational Chatbot

TL;DR

This paper presents a jewelry shop chatbot that uses intent recognition and audio input to generate natural, contextually relevant responses, improving upon traditional retrieval-based and generative chatbots.

Contribution

It introduces an intelligent conversational system capable of understanding unseen queries and generating follow-up remarks, with an audio interface for natural language interaction.

Findings

Achieved high Recall, Precision, and F1 scores in intent detection.

Enabled natural language spoken interaction with the chatbot.

Enhanced response relevance and conversational flow.

Abstract

Since the advent of chatbots in the commercial sector, they have been widely employed in the customer service department. Typically, these commercial chatbots are retrieval-based, so they are unable to respond to queries absent in the provided dataset. On the contrary, generative chatbots try to create the most appropriate response, but are mostly unable to create a smooth flow in the customer-bot dialog. Since the client has few options left for continuing after receiving a response, the dialog becomes short. Through our work, we try to maximize the intelligence of a simple conversational agent so it can answer unseen queries, and generate follow-up questions or remarks. We have built a chatbot for a jewelry shop that finds the underlying objective of the customer's query by finding similarity of the input to patterns in the corpus. Our system features an audio input interface for clients, so they may speak to it in natural language. After converting the audio to text, we trained the model to extract the intent of the query, to find an appropriate response and to speak to the client in a natural human voice. To gauge the system's performance, we used performance metrics such as Recall, Precision and F1 score.