Breakdowns and Repairs. Detecting Patterns that Lead to Breakdowns in Customer Service Messages (2023)

Abstract: Many companies use dialogue systems for their customer service, and although there has been a rise in the usage of these systems (Costello and LoDolce, 2022), many of these systems still face challenges in comprehending and properly responding to the customer (Følstadet al., 2021). In our project we aim to figure out how to develop and improve these conversational agents. Part of this project (detailed in this paper) will focus on the detection of breakdown patterns and the possible solutions (repairs) to mitigate negative results of these errors.

## research interests many companies use dialogue systems for their customer service, and although there has been a rise in the usage of these systems @xcite , many of these systems still face challenges in comprehending and properly responding to the customer @xcite . in our project 1 we aim to figure out how to develop and improve these conversational agents. part of this project, focuses on the detection of breakdown patterns and the possible solutions (repairs) to mitigate negative results of these errors. ## spoken dialogue system (sds) research since my submission last year, much has changed within the field of dialogue systems. with the advent of chat-gpt and subsequent open alternatives (such as alpaca @xcite and open assistant), there has been renewed (media)attention for dialogue systems and chatbots. these new technologies will bring new possibilities for research into dialogue systems but also new challenges. i suspect that much more research will focus on the challenges and problems these systems will bring, in for example the context of education @xcite and hospitality @xcite . i also suspect that the (general) public gets more and more familiar with these systems and the (assumed) capabilities of systems like chatgpt. due to both positive and negative attention to these technologies, expectations of the public towards dialogue systems will also shift. therefore it seems important to learn more about expectations of users and the ways in which we can manage those expectations. previous research has already shown that expectation management is an important factor. for a chatbot to be successful the user needs to know what to expect from the beginning @xcite . previous work has also stressed the importance of understanding the user perceptions and expectations before building the chatbot @xcite , and creating chatbots with characteristics that are in line with users' expectations @xcite . users tend to evaluate chatbots worse when the experience does not line up with their expectations @xcite . similar research is now also done with chatgpt, for example surveying the expectations of healthcare workers on adopting chatgpt in their work @xcite . ## suggested topics for discussion breakdowns and repairs can we mitigate negative effects after encountering erroneous chatbots with only repairs or are there other solutions as well? should we tailor repairs to specific situations or breakdowns?