  We study question-answering over semi-structured data. We introduce a new way
to apply the technique of semantic parsing by applying machine learning only to
provide annotations that the system infers to be missing; all the other parsing
logic is in the form of manually authored rules. In effect, the machine
learning is used to provide non-syntactic matches, a step that is ill-suited to
manual rules. The advantage of this approach is in its debuggability and in its
transparency to the end-user. We demonstrate the effectiveness of the approach
by achieving state-of-the-art performance of 40.42% accuracy on a standard
benchmark dataset over tables from Wikipedia.
