  Row completion is the task of augmenting a given table of text and numbers
with additional, relevant rows. The task divides into two steps: subject
suggestion, the task of populating the main column; and gap filling, the task
of populating the remaining columns. We present state-of-the-art results for
subject suggestion and gap filling measured on a standard benchmark
(WikiTables). Our idea is to solve this task by harmoniously combining
knowledge base table interpretation and free text generation. We interpret the
table using the knowledge base to suggest new rows and generate metadata like
headers through property linking. To improve candidate diversity, we synthesize
additional rows using free text generation via GPT-3, and crucially, we exploit
the metadata we interpret to produce better prompts for text generation.
Finally, we verify that the additional synthesized content can be linked to the
knowledge base or a trusted web source such as Wikipedia.
