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

Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations