# A Survey on Multi-User Conversational Interfaces
## 4 Capturing Multi-User Conversational Interfaces
### User and Dialogue Modelling  
While traditional systems employed models with pre-defined rules and modular approaches [15], current modern MUCIs use data-driven methods, substituting some of the components (e.g., in [34] or integrating end-to-end pipelines (e.g., in [18; 19] where neural networks are used to model the addressee and response selection of the system). However, as commented before, the decision of using one approach or another also depends on the task and final target. For example, in critical domains such as mental health, complete data-driven approaches could result in catastrophic consequences; for this reason, the application domain plays a key role in the design decisions. In this section, we examine different approaches followed in the literature of our survey on user and dialogue modelling. A user model is a representation of personal data collected from users, which usually consists of categories such as their preferences, relevant personal traits, or previous interactions. The aim is to equip a conversational interface with knowledge about users, thus enabling an adaptation of its conversation behaviour and response generation to specific characteristics. While traditional approaches contained only static information, contemporary models are updated dynamically during interactions. In parallel, dialogue modelling refers to the task of defining an interaction policy, thus determining which system action a conversational interface has to perform next. Since this decision-making frequently depends on previous user input, dialogue models typically include a dialogue history containing interaction-specific data. One particular challenge in multi-user conversational systems is turn-taking, which means at which time and which message a system should send to its users [3]. Here, research faces the problem of detecting clear turn-taking signals, in contrast to a two-person (dyadic) conversation where indicators such as pauses and gaze directions are relatively easy to detect. In multi-user conversations, these signals are often subtle and ambiguous, making it difficult to accurately determine when one speaker has finished and another is likely to start. In a dyadic dialogue, it is usually clear when the role of the speaker changes, whereas in multi-user dialogues, additional roles like side participant or overhearer further complicate the identification of the (next) speaker. Another issue is based on crosstalk: in spoken multi-user conversations, speakers often overlap, interrupt, and talk simultaneously, making it complex to distinguish between turn transitions and backchannelling (e.g., 'ok', or 'well') [11]. Moreover, the fact that people have different speaking styles, speech rates, and utterance durations makes it challenging to model turn-taking dynamics in a MU conversation. Some individuals tend to speak more often or for longer durations, while others may remain silent or speak less frequently. Turn-taking in MU conversations is also often influenced by contextual and social factors such as the topic of discussion, the participants' roles and status, and their prior relationships. These factors can affect turn-taking behaviour, making it difficult to develop models that can accommodate these aspects. Multi-user conversations quickly become more complex as the number of users increases, since functions like face recognition and gaze tracking can put heavy computational requirements on the system. In general, there is so far no golden rule for modelling the turn-taking in Multi-User Conversational Interfaces, and as a result, different system prototypes were proposed in the reviewed literature. Table 3 presents an overview of the user and dialogue modelling approaches found in the reviewed publications. [PERSON] et al. [17] present a survey of various methods for engaging conversational agents, including user-centred evaluation approaches. In the domain of multilingual MU dialogue systems, [PERSON] et al.