# A Survey on Multi-User Conversational Interfaces
## 1 Introduction  
The vision of enabling humans to communicate with machines existed well before the past century. However, it was the development of advanced computers and processing methods that made the transition from concepts to reality possible. Lately, remarkable progress has been made in allowing one single user to access computer applications via Conversational User Interfaces (CUIs), yet most of contemporary work neglects an important aspect of natural human behaviour--interactions in groups. The goal of this paper is therefore to provide a comprehensive overview of research on CUIs that focus on interactions with multiple users simultaneously. Research on CUIs aims to develop systems which simulate human-like interactions through natural language-processing techniques, enabling intuitive dialogues between humans and machines. In literature, there exist various terms to describe such a system: dialogue system, conversational agent, or chatbot, just to name a few. Although eachterm is associated with a specific definition, they are often used interchangeably within the research community. In this work, a CUI is referred to as a user interface that allows its users to interact with a computer application using natural language dialogue, either via text, spoken language, or a combination of both. Figure 1 displays the traditional pipeline architecture of single-user CUIs, which consisted of 5 distinct modules responsible for completing domain-dependent tasks. First, the Automatic Speech Recognition (ASR) converts speech to text to enable spoken language interactions. In a next processing step, the Natural Language Understanding (NLU) module analyses the input text to extract meaning and semantic information, such as intent of the user and relevant entities. Subsequently, the dialogue management decides the next action or response in accordance with its dialogue policy by considering the current state of the conversation, the user intent combined with their preferences, and the context of the conversation. This component manages the flow of the conversation and also decides whether to access external services or knowledge sources relevant for the task, defining the behaviour of the system in a conversation. The system action (e.g., request for more relevant information, or details about ongoing tasks) is later processed by Natural Language Generation (NLG), which is in charge of transforming the semantic system actions back into natural language. In cases where a speech-based interface is used, the last step of the pipeline is the Text-To-Speech (TTS) component that converts the textual response into a speech signal. Contemporary systems also aim to employ end-to-end training to replace the modular architecture, or hybrid approaches to maintain control over the dialogue sequence and generations. In contrast to traditional single-user interfaces, where the focus is on one-to-one interactions between a user and a system, Multi-User Conversational Interfaces (MUCIs) are required to interpret input and handle the conversation for several users at the same time. This competence introduces an additional level of complexity, which makes it necessary to equip MUCIs with skills for turn-taking between the system and different users, context awareness of all the implicated parties, and in-group dynamics, as can be seen in Figure 2.  
To maintain a coherent dialogue flow, the system must be able to determine when it is addressed, whether it should respond, and to which user. Since these requirements pose major challenges for the development and implementation, it is essential to intensify research, enabling the components to recognise context within group interactions to appropriately manage the dialogue flow. However, apart from limited resources such as computing power or suitable training data, there is another complicating factor: the guidelines for the design of MUCIs are largely undefined or derived from concepts of human-machine interaction for a single user, which is impractical for many applications.