  Large Scale Question-Answering systems today are widely used in downstream
applications such as chatbots and conversational dialogue agents. Typically,
such systems consist of an Answer Passage retrieval layer coupled with Machine
Comprehension models trained on natural language query-passage pairs. Recent
studies have explored Question Answering over structured data sources such as
web-tables and relational databases. However, architectures such as Seq2SQL
assume the correct table a priori which is input to the model along with the
free text question. Our proposed method, analogues to a passage retrieval model
in traditional Question-Answering systems, describes an architecture to discern
the correct table pertaining to a given query from amongst a large pool of
candidate tables.
