  Complex question-answering (CQA) involves answering complex natural-language
questions on a knowledge base (KB). However, the conventional neural program
induction (NPI) approach exhibits uneven performance when the questions have
different types, harboring inherently different characteristics, e.g.,
difficulty level. This paper proposes a meta-reinforcement learning approach to
program induction in CQA to tackle the potential distributional bias in
questions. Our method quickly and effectively adapts the meta-learned
programmer to new questions based on the most similar questions retrieved from
the training data. The meta-learned policy is then used to learn a good
programming policy, utilizing the trial trajectories and their rewards for
similar questions in the support set. Our method achieves state-of-the-art
performance on the CQA dataset (Saha et al., 2018) while using only five trial
trajectories for the top-5 retrieved questions in each support set, and
metatraining on tasks constructed from only 1% of the training set. We have
released our code at https://github.com/DevinJake/MRL-CQA.
