Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model
This work addresses table augmentation for data enrichment, offering an incremental improvement over existing methods.
The paper tackles the row completion task by combining knowledge base interpretation with GPT-3 text generation, achieving state-of-the-art results on the WikiTables benchmark.
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.