  In this paper, we describe a dataset and baseline result for a question
answering that utilizes web tables. It contains commonly asked questions on the
web and their corresponding answers found in tables on websites. Our dataset is
novel in that every question is paired with a table of a different signature.
In particular, the dataset contains two classes of tables: entity-instance
tables and the key-value tables. Each QA instance comprises a table of either
kind, a natural language question, and a corresponding structured SQL query. We
build our model by dividing question answering into several tasks, including
table retrieval and question element classification, and conduct experiments to
measure the performance of each task. We extract various features specific to
each task and compose a full pipeline which constructs the SQL query from its
parts. Our work provides qualitative results and error analysis for each task,
and identifies in detail the reasoning required to generate SQL expressions
from natural language questions. This analysis of reasoning informs future
models based on neural machine learning.
