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

Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection