Parsing natural language to corresponding SQL (NL2SQL) with data driven
approaches like deep neural networks attracts much attention in recent years.
Existing NL2SQL datasets assume that condition values should appear exactly in
natural language questions and the queries are answerable given the table.
However, these assumptions may fail in practical scenarios, because user may
use different expressions for the same content in the table, and query
information outside the table without the full picture of contents in table.
Therefore we present TableQA, a large-scale cross-domain Natural Language to
SQL dataset in Chinese language consisting 64,891 questions and 20,311 unique
SQL queries on over 6,000 tables. Different from exisiting NL2SQL datasets,
TableQA requires to generalize well not only to SQL skeletons of different
questions and table schemas, but also to the various expressions for condition
values. Experiment results show that the state-of-the-art model with 95.1%
condition value accuracy on WikiSQL only gets 46.8% condition value accuracy
and 43.0% logic form accuracy on TableQA, indicating the proposed dataset is
challenging and necessary to handle. Two table-aware approaches are proposed to
alleviate the problem, the end-to-end approaches obtains 51.3% and 47.4%
accuracy on the condition value and logic form tasks, with improvement of 4.7%
and 3.4% respectively.

本文介绍了 TableQA dataset 及其挑战性和必要性，探讨了解决不同条件值表述和表格外查询的问题的两种基于表格知识的方法，并给出实验结果。