# Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review
## 1 Introduction
For instance, users' expectations are usually very high since they want to communicate with the system in the same way as they interact with other human beings. The conversational system therefore has to deal with ambiguities that might even be interpreted differently by different people [19]. Moreover, most NLIs for visualisation started out relying on limited jargon (i.e., vocabulary based on specific data), simple visualisations (e.g., bar charts, line charts) and functions such as filtering and selection. For instance, [PERSON] et al., who were pioneers in the field, proposed a basic system using _form-based_ interaction, meaning that users typed their queries (analytical intents) into a text box in order to obtain the corresponding visualisation outputs [20]. As research has advanced, more sophisticated V-NLIs have been developed, such as those referred to as _chatbot-based_. Chatbots are intelligent conversational systems that not only provide visual outputs to users but also guide them, especially users with less experience in visual analytics, with additional aids such as textual feedback, recommendations, and complex multi-stepped queries [21]. Despite V-NLI being a relatively new field, several survey papers have already addressed this topic. [PERSON] et al. [12] presented a broad review of NLIs for visualisation. They summarised various features of NLIs including query interpretation, human interaction and dialogue management to highlight existing gaps in the field. Moreover, they analysed a variety of NLIs for visualisation including simple (one turn interactions), conversational (systems that track the conversation with follow-up questions) and narrative storytelling (systems that show multiple visualisations side by side with annotations). Other reviews of the literature have focused on specific aspects of V-NLIs. [PERSON] et al. [11] proposed three task-based categories: visualisation-related tasks, data related tasks and system-control-related tasks. Moreover, a recent systematic review [22] analysed NLIs both for databases and for data visualisations in terms of input and output. On the input side, they examined multimodality and different types of queries, such as open-ended or factual. On the output side, they considered those that give textual answers, generate new visualisations and interact with existing ones. In summary, previous works have focused on reviewing Visualisation-oriented Natural Language Interfaces that were mainly conceived as _form-based_ question-answering systems, where the users ask the system questions using UI (User Interface) widgets, and the system's answer takes the form of text, a filtered visualisation and/or a new visualisation. Nevertheless, recent advances in Natural Language Processing have facilitated a double enhancement of these systems, both in its inner workings (NLU--Natural Language Understanding and NLC--Natural Language Generation) and in its interface. The interface is now a chatbot (embodied or not) that engages in conversation with the users to facilitate their interaction with visualisations. To the best of our knowledge, there has been no attempt in the V-NLI literature to specifically examine the relationship between the fields of data visualisation and chatbots. Thus, this paper presents a scoping review that analyses synergies between both fields and also summarises knowledge gained in analysing research works that have proposed _chutbot-based_ V-NLIs for data visualisation. Our contribution is as follows:  
* We present a scoping review to study the synergies between both data visualisation and chatbots fields to analyse how the use of chatbots improved data visualisation and visual analysis. * We propose an analysis framework based on the three spaces of the data visualisation pipeline, i.e., Data Space, Visual Space and Interaction Space as well as on a characterisation of chatbots using four dimensions called AINT (A--Anthropomorphic, I--Intelligence, N--Natural Language Processing, and T--inTeractivity).