We propose a new end-to-end question answering model, which learns to
aggregate answer evidence from an incomplete knowledge base (KB) and a set of
retrieved text snippets. Under the assumptions that the structured KB is easier
to query and the acquired knowledge can help the understanding of unstructured
text, our model first accumulates knowledge of entities from a question-related
KB subgraph; then reformulates the question in the latent space and reads the
texts with the accumulated entity knowledge at hand. The evidence from KB and
texts are finally aggregated to predict answers. On the widely-used KBQA
benchmark WebQSP, our model achieves consistent improvements across settings
with different extents of KB incompleteness.

提出一种新的端到端问答模型，使用不完整知识库和一组检索到的文本片段学习如何汇聚答案证据，首先在与问题相关的知识库子图中累积实体知识，然后在潜在空间中重构问题并根据手头的累积实体知识读取文字，最后汇总来自知识库和文本的证据以预测答案，在广泛使用的 KBQA 基准 WebQSP 上，我们的模型在不同程度 KB 不完整的情况下均实现了一致的改进。