  It is common for people to create different types of charts to explore a
multi-dimensional dataset (table). However, to recommend commonly composed
charts in real world, one should take the challenges of efficiency, imbalanced
data and table context into consideration. 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 165k
tables and 266k charts, we show that Table2Charts could learn a shared
representation of table fields so that recommendation tasks on different chart
types could mutually enhance each other. Table2Charts outperforms other chart
recommendation systems in both multi-type task (with doubled recall numbers
<EMAIL_ADDRESS>and<EMAIL_ADDRESS>and human evaluations.
