Generally speaking, weakly supervised semantic parsers operate on one assumption -a candidate semantic parse is treated as a correct one if it results in answers that are identical to the gold answers.
a straightforward modification of existing structured learning algorithms in our setting is to use any semantic parse found to evaluate to the correct answers during beam search as a reference parse, and then update the model parameters accordingly.