Abstract
Neural Program Induction (NPI) is a paradigm
for decomposing high-level tasks such as complex
question-answering over knowledge bases (KBQA)
into executable programs by employing neural
models. Typically, this involves two key phases:
i) inferring input program variables from the highlevel task description, and ii) generating the correct
program sequence involving these variables. Here
we focus on NPI for Complex KBQA with only the
final answer as supervision, and not gold programs.
This raises major challenges; namely i) noisy query
annotation in the absence of any supervision can
lead to catastrophic forgetting while learning, ii) reward becomes extremely sparse owing to the noise.
To deal with these, we propose a noise-resilient
NPI model, Stable Sparse Reward based Programmer (SSRP) that evades noise-induced instability
through continual retrospection and its comparison
with current learning behavior. On complex KBQA
datasets, SSRP performs at par with hand-crafted
rule-based models when provided with gold program input, and in the noisy settings outperforms
state-of-the-art models by a significant margin even
with a noisier query annotator