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The predominant challenge in weakly supervised semantic parsing is that ofspurious programs that evaluate to correct answers for the wrong reasons. Priorwork uses elaborate search strategies to mitigate the prevalence of spuriousprograms; however, they typically consider only one input at a time. In thiswork we explore the use of consistency between the output programs for relatedinputs to reduce the impact of spurious programs. We bias the program search(and thus the model's training signal) towards programs that map the samephrase in related inputs to the same sub-parts in their respective programs.Additionally, we study the importance of designing logical formalisms thatfacilitate this kind of consAistency-based training. We find that a moreconsistent formalism leads to improved model performance even withoutconsistency-based training. When combined together, these two insights lead toa 10% absolute improvement over the best prior result on the Natural LanguageVisual Reasoning dataset.

Enforcing Consistency in Weakly Supervised Semantic Parsing