Improving chatbot design and intent recognition: An approach through the methods of intercultural pragmatics

Abstract Designing chatbots that provide a good user experience, guide users to their goals, and are inclusive and accessible is vital to ensure that key public and commercial services are available to a wide range of users who hold varying social norms and exhibit different patterns of social interaction. However, chatbot design can be undermined by a lack of consideration of user needs and LLMs that are not fine-tuned to account for these differences. Using examples from the testing and development of a medical appointment booking chatbot, this paper showcases how the established methods, approaches and insights of intercultural pragmatics can be used to optimize the dialogue design of chatbots and improve the intent recognition of the language models which drive them. Specifically, this work draws on the CCSARP framework for classification of requests and the GAAFFE framework, which were applied to both naturally occurring and simulated data. The paper also discusses the boundaries and limitations of these approaches.