Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games
This addresses the challenge of interpretable reasoning in multi-hop QA for AI systems, though it appears incremental as it builds on existing datasets and methods.
The paper tackles the problem of recovering reasoning chains for multi-hop question answering using only question-answer pairs as weak supervision, proposing a cooperative game approach that selects and connects evidence passages to identify confident chains, with experimental results showing effectiveness on benchmarks like HotpotQA and MedHop.
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.