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