  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.
