  Designing a reliable natural language (NL) interface for querying tables has
been a longtime goal of researchers in both the data management and natural
language processing (NLP) communities. Such an interface receives as input an
NL question, translates it into a formal query, executes the query and returns
the results. Errors in the translation process are not uncommon, and users
typically struggle to understand whether their query has been mapped correctly.
We address this problem by explaining the obtained formal queries to non-expert
users. Two methods for query explanations are presented: the first translates
queries into NL, while the second method provides a graphic representation of
the query cell-based provenance (in its execution on a given table). Our
solution augments a state-of-the-art NL interface over web tables, enhancing it
in both its training and deployment phase. Experiments, including a user study
conducted on Amazon Mechanical Turk, show our solution to improve both the
correctness and reliability of an NL interface.
