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Exploring Lexical Alignment Influences in Price Bargain Chatbot

Zhao, Zhenqi (2024) Exploring Lexical Alignment Influences in Price Bargain Chatbot.

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Abstract:This study investigates the integration of lexical alignment into text-based negotiation chatbots, including its impact on user perception and potential influences on negotiation results. Lexical alignment, where participants in a conversation adopt similar words, has been evidenced by previous studies to potentially enhance chatbot user experiences. This study proposed a chatbot architecture designed for price bargaining, incorporating lexical alignment. The architecture consists of seven components: user interface, intent classifier, price extractor, product extractor, dialogue management, response generation, and database. Key among these are the intent classifier, which categorizes user inputs; the price and product extractors, which identify prices and products mentioned in the conversation; dialogue management, which follows predefined rules for chatbot actions; and response generation, which crafts appropriate responses using OpenAI's API and prompt engineering techniques. This response generation can include lexical alignment features or not. To evaluate the effects of lexical alignment, a user experiment was conducted. This between-subject experiment was carried out online through a website where the participants were briefly introduced to the study without revealing its main purpose to avoid bias. A total of 52 individuals participated. While the results did not show statistical significance, they suggest that lexical alignment might positively influence user satisfaction. This finding indicates a potential direction for enhancing user interaction with chatbots in the future.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Business Information Technology MSc (60025)
Link to this item:https://purl.utwente.nl/essays/98233
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