It is common for people to create different types of charts to explore a multi-dimensional dataset (table). However, to build an intelligent assistant that recommends commonly composed charts, the fundamental problems of "multi-dialect" unification, imbalanced data and open vocabulary exist. In this paper, we propose Table2Charts framework which learns common patterns from a large corpus of (table, charts) pairs. Based on deep Q-learning with copying mechanism and heuristic searching, Table2Charts does table-to-sequence generation, where each sequence follows a chart template. On a large spreadsheet corpus with 196k tables and 306k charts, we show that Table2Charts could learn a shared representation of table fields so that tasks on different chart types could mutually enhance each other. Table2Charts has >0.61 recall at top-3 and >0.49 recall at top-1 for both single-type and multi-type chart recommendation tasks.

本文介绍了一个名为Table2Charts的框架，通过深度Q学习和启发式搜索实现了表格数据到图表的自动生成，它能够在考虑数据分布和表格上下文信息的情况下推荐生成符合数据特点的图表，并在165k表格和266k图表的大型数据集上表现出了优异的图表推荐能力，其在多类型任务和人工评估方面均表现出较高水平。

Table2Charts: 通过学习共享表格表示推荐图表