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Multi-hop Knowledge Base Question Answering (KBQA) aims to find the answerentities that are multiple hops away in the Knowledge Base (KB) from theentities in the question. A major challenge is the lack of supervision signalsat intermediate steps. Therefore, multi-hop KBQA algorithms can only receivethe feedback from the final answer, which makes the learning unstable orineffective.  To address this challenge, we propose a novel teacher-student approach forthe multi-hop KBQA task. In our approach, the student network aims to find thecorrect answer to the query, while the teacher network tries to learnintermediate supervision signals for improving the reasoning capacity of thestudent network. The major novelty lies in the design of the teacher network,where we utilize both forward and backward reasoning to enhance the learning ofintermediate entity distributions. By considering bidirectional reasoning, theteacher network can produce more reliable intermediate supervision signals,which can alleviate the issue of spurious reasoning. Extensive experiments onthree benchmark datasets have demonstrated the effectiveness of our approach onthe KBQA task. The code to reproduce our analysis is available athttps://github.com/RichardHGL/WSDM2021_NSM.

Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals