  Semantic parsing from denotations faces two key challenges in model training:
(1) given only the denotations (e.g., answers), search for good candidate
semantic parses, and (2) choose the best model update algorithm. We propose
effective and general solutions to each of them. Using policy shaping, we bias
the search procedure towards semantic parses that are more compatible to the
text, which provide better supervision signals for training. In addition, we
propose an update equation that generalizes three different families of
learning algorithms, which enables fast model exploration. When experimented on
a recently proposed sequential question answering dataset, our framework leads
to a new state-of-the-art model that outperforms previous work by 5.0% absolute
on exact match accuracy.
