  Tables in Web documents are pervasive and can be directly used to answer many
of the queries searched on the Web, motivating their integration in question
answering. Very often information presented in tables is succinct and hard to
interpret with standard language representations. On the other hand, tables
often appear within textual context, such as an article describing the table.
Using the information from an article as additional context can potentially
enrich table representations. In this work we aim to improve question answering
from tables by refining table representations based on information from
surrounding text. We also present an effective method to combine text and
table-based predictions for question answering from full documents, obtaining
significant improvements on the Natural Questions dataset.
