Exploring Lexical Alignment in a Price Bargain Chatbot
Article No.: 40, Pages 1 - 7
Editorial Notes
The authors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected VoR was published on November 18, 2024. For reference purposes the VoR may still be accessed via the Supplemental Material section on this page.
Abstract
This study investigates the integration of lexical alignment into text-based negotiation chatbots, including its impact on user satisfaction, perceived trustworthiness, and potential influences on negotiation results. Lexical alignment is the phenomenon where participants in a conversation adopt similar words. This study introduces a chatbot architecture for price negotiation, consisting of components such as intent and price/product extractors, dialogue management, and response generation using OpenAI’s API, with a lexical alignment feature. To evaluate the effects of lexical alignment on negotiation outcomes and the user’s perception of the chatbot, a between-subject user experiment was conducted online. A total of 52 individuals participated. While the results do 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.
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Index Terms
- Exploring Lexical Alignment in a Price Bargain Chatbot
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Published In
July 2024
616 pages
ISBN:9798400705113
DOI:10.1145/3640794
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Published: 08 July 2024
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