End-to-end question answering using a differentiable knowledge graph is a
promising technique that requires only weak supervision, produces interpretable
results, and is fully differentiable. Previous implementations of this
technique (Cohen et al., 2020) have focused on single-entity questions using a
relation following operation. In this paper, we propose a model that explicitly
handles multiple-entity questions by implementing a new intersection operation,
which identifies the shared elements between two sets of entities. We find that
introducing intersection improves performance over a baseline model on two
datasets, WebQuestionsSP (69.6% to 73.3% Hits@1) and ComplexWebQuestions (39.8%
to 48.7% Hits@1), and in particular, improves performance on questions with
multiple entities by over 14% on WebQuestionsSP and by 19% on
ComplexWebQuestions.

该论文提出了一种新的模型，使用可微的知识图来回答多个实体问题，并通过引入交集操作来提高性能。研究结果表明，该模型可在两个数据集上提高性能，特别是在多实体问题上，相比基准模型提高了 14% 至 19%。