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arXiv · 2109.05808

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

Priyanka Sen·Amir Saffari·Armin Oliya
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Abstract

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.

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Priyanka Sen, Amir Saffari, Armin Oliya. 2021-09-13. Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection. https://arxiv.org/abs/2109.05808

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