# Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review
## 5 Discussion
### RQ2: How Do Chatbot-Based V-NLIs Contribute to Interactions with the Visual Space? By addressing this research question, we aim to shed light on how V-NLIs in the literature (Tables 4 and 5) can support users' tasks in the Visual Space (Table 2) of the visualisation pipeline. Figure 11 shows the scope of advanced and basic visualisations in both V-NLI and visualisation dimensions; see the borders in purple and green colour, respectively. As we can appreciate in the magenta- and blue-coloured polygons, V-NLIs that consider basic layouts embrace these dimensions in greater measure than those considering advanced layouts. Furthermore, the empty space of the spider reveals that there is a lot of room for research on different aspects of both basic and advanced visualisations in V-NLIs. This gap can be explained by the fact that the field is still in its early stages of development, and consequently, many researchers focused on exploring the foundational aspects of the technology. Moreover, the reviewed research works usually concentrated on one aspect of the V-NLI at a time. For example, some works investigated query recommendation [40; 113; 114], others explored multimodality [15; 88], whereas others focused on designing personalised V-NLIs for specific data and user profiles such as data scientists [86; 87]. It is especially interesting to focus the analysis on conversational guidance strategies (Auto-complete, Help, Recommendation and Follow-up; see Figure 11, purple arc) since they improve the interpretability/understanding of the NLI query, guide the user along the process of the analysis, and so have a positive impact on the whole user experience. During this review, we came across works that include different kinds of conversational guidance [15; 39; 40; 63; 64; 69; 81; 84; 85; 87; 91; 92], few of them supporting multiple types [40; 81]. Nevertheless, there is an unexplored aspect in these works, which is that guidance strategies can be designed by focusing on the visualisation pipeline. Indeed, this aspect allows us to analyse this RQ but in reverse: \"_How the can visualisation process contribute to improve V-NLIs?_\". In this context, a recent research project proposes the so-called eXplainable NLI (XNLI) [115], which is based on a high-level grammar for statistical graphics (Vega-lite specification [116]). Thanks to this grammar, the system is able to provide the users with explanations of the following visualisation process as well as tips for interactively reviewing the natural language-based query. We firmly believe that this idea can be extended to more advanced visualisations thanks to recent proposals such as GoTree [117], a grammar that allows tree visualisations to be instantiated by specifying different aspects such as visual elements, layouts and coordinate systems. **Remark 4**. : _Clutbots' guidance strategies can be designed leaning on the visualisation pipeline. That is, \"How can the knowledge about the visualisation process improve V-NLIs?\"_  
Another way to facilitate a visual analysis, especially for inexperienced users, is to perform an automatic Visual Mapping, i.e., selecting the visualisation layouts and graphical elements automatically. When we explored related works, we found that most of the V-NLIs that support advanced visualisations do so with fixed layouts (see Figure 11, dark green arc). There is only one V-NLI that visualises an advanced visualisation (parallel plots) according to a rule-based visual mapping [88]. One possible reason for this lack of works may be that selecting visualisation layouts and graphical elements automatically is a complex task since it requires the V-NLI, first, to interpret user input accurately and, second, select the appropriate visualisation method based both on the data and on the context of the conversation. Moreover, most of the works that use rule-based visual mapping identification are form-based V-NLIs. Note that only one study included in this scoping review explored intelligent Visual Mapping [83].