  Compositional reasoning tasks like multi-hop question answering, require
making latent decisions to get the final answer, given a question. However,
crowdsourced datasets often capture only a slice of the underlying task
distribution, which can induce unanticipated biases in models performing
compositional reasoning. Furthermore, discriminatively trained models exploit
such biases to get a better held-out performance, without learning the right
way to reason, as they do not necessitate paying attention to the question
representation (conditioning variable) in its entirety, to estimate the answer
likelihood. In this work, we propose a generative context selection model for
multi-hop question answering that reasons about how the given question could
have been generated given a context pair. While being comparable to the
state-of-the-art answering performance, our proposed generative passage
selection model has a better performance (4.9% higher than baseline) on
adversarial held-out set which tests robustness of model's multi-hop reasoning
capabilities.
