Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language Conversations

TL;DR

This paper introduces CoVis, a new task for interactive data visualization through natural language dialogues, along with a benchmark dataset and a neural network model that outperforms existing methods.

Contribution

It proposes the novel CoVis task, creates Dial-NVBench dataset, and develops MMCoVisNet, a multi-modal neural network for conversational data visualization generation.

Findings

MMCoVisNet outperforms baseline models on Dial-NVBench.

The benchmark enables evaluation of dialogue-based visualization systems.

The approach effectively understands dialogue context and generates appropriate visualizations.

Abstract

Data visualization (DV) has become the prevailing tool in the market due to its effectiveness into illustrating insights in vast amounts of data. To lower the barrier of using DVs, automatic DV tasks, such as natural language question (NLQ) to visualization translation (formally called text-to-vis), have been investigated in the research community. However, text-to-vis assumes the NLQ to be well-organized and expressed in a single sentence. However, in real-world settings, complex DV is needed through consecutive exchanges between the DV system and the users. In this paper, we propose a new task named CoVis, short for Conversational text-to-Visualization, aiming at constructing DVs through a series of interactions between users and the system. Since it is the task which has not been studied in the literature, we first build a benchmark dataset named Dial-NVBench, including dialogue sessions with a sequence of queries from a user and responses from the system. Then, we propose a multi-modal neural network named MMCoVisNet to answer these DV-related queries. In particular, MMCoVisNet first fully understands the dialogue context and determines the corresponding responses. Then, it uses adaptive decoders to provide the appropriate replies: (i) a straightforward text decoder is used to produce general responses, (ii) an SQL-form decoder is applied to synthesize data querying responses, and (iii) a DV-form decoder tries to construct the appropriate DVs. We comparatively evaluate MMCoVisNet with other baselines over our proposed benchmark dataset. Experimental results validate that MMCoVisNet performs better than existing baselines and achieves a state-of-the-art performance.