Text2Chart: A Multi-Staged Chart Generator from Natural Language Text

TL;DR

Text2Chart is a multi-staged method that converts natural language analytical text into appropriate 2D charts, involving entity recognition, mapping, and chart type prediction, with promising experimental results.

Contribution

The paper introduces a novel multi-staged approach for generating visualizations from natural language text, including a new dataset and effective model combinations.

Findings

BERT-based encodings improve entity recognition accuracy

Random Forest effectively maps x- and y-entities

fastText with LSTM accurately predicts chart types

Abstract

Generation of scientific visualization from analytical natural language text is a challenging task. In this paper, we propose Text2Chart, a multi-staged chart generator method. Text2Chart takes natural language text as input and produce visualization as two-dimensional charts. Text2Chart approaches the problem in three stages. Firstly, it identifies the axis elements of a chart from the given text known as x and y entities. Then it finds a mapping of x-entities with its corresponding y-entities. Next, it generates a chart type suitable for the given text: bar, line or pie. Combination of these three stages is capable of generating visualization from the given analytical text. We have also constructed a dataset for this problem. Experiments show that Text2Chart achieves best performances with BERT based encodings with LSTM models in the first stage to label x and y entities, Random Forest classifier for the mapping stage and fastText embedding with LSTM for the chart type prediction. In our experiments, all the stages show satisfactory results and effectiveness considering formation of charts from analytical text, achieving a commendable overall performance.