  The predominant challenge in weakly supervised semantic parsing is that of
spurious programs that evaluate to correct answers for the wrong reasons. Prior
work uses elaborate search strategies to mitigate the prevalence of spurious
programs; however, they typically consider only one input at a time. In this
work we explore the use of consistency between the output programs for related
inputs to reduce the impact of spurious programs. We bias the program search
(and thus the model's training signal) towards programs that map the same
phrase in related inputs to the same sub-parts in their respective programs.
Additionally, we study the importance of designing logical formalisms that
facilitate this kind of consAistency-based training. We find that a more
consistent formalism leads to improved model performance even without
consistency-based training. When combined together, these two insights lead to
a 10% absolute improvement over the best prior result on the Natural Language
Visual Reasoning dataset.
