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

Representations for Question Answering from Documents with Tables and Text