  We propose the new problem of learning to recover reasoning chains from
weakly supervised signals, i.e., the question-answer pairs. We propose a
cooperative game approach to deal with this problem, in which how the evidence
passages are selected and how the selected passages are connected are handled
by two models that cooperate to select the most confident chains from a large
set of candidates (from distant supervision). For evaluation, we created
benchmarks based on two multi-hop QA datasets, HotpotQA and MedHop; and
hand-labeled reasoning chains for the latter. The experimental results
demonstrate the effectiveness of our proposed approach.
