  A compelling approach to complex question answering is to convert the
question to a sequence of actions, which can then be executed on the knowledge
base to yield the answer, aka the programmer-interpreter approach. Use similar
training questions to the test question, meta-learning enables the programmer
to adapt to unseen questions to tackle potential distributional biases quickly.
However, this comes at the cost of manually labeling similar questions to learn
a retrieval model, which is tedious and expensive. In this paper, we present a
novel method that automatically learns a retrieval model alternately with the
programmer from weak supervision, i.e., the system's performance with respect
to the produced answers. To the best of our knowledge, this is the first
attempt to train the retrieval model with the programmer jointly. Our system
leads to state-of-the-art performance on a large-scale task for complex
question answering over knowledge bases. We have released our code at
https://github.com/DevinJake/MARL.
