论文标题
学会通过合作游戏恢复多跳问题的推理链
Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games
论文作者
论文摘要
我们提出了学习的新问题，即从弱监督的信号（即提问 - 答案对）中恢复推理链。我们提出了一种合作游戏方法来解决这个问题，其中如何选择证据段落以及如何连接所选段落，由两种合作来处理，这些模型可以从大量候选人（来自遥远的监督）中选择最自信的链条。为了进行评估，我们基于两个多跳QA数据集创建了基准测试，HotPotQA和MedHop；以及后者的手工标记的推理链。实验结果证明了我们提出的方法的有效性。
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.
