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Recently, end-to-end (E2E) trained models for question answering overknowledge graphs (KGQA) have delivered promising results using only a weaklysupervised dataset. However, these models are trained and evaluated in asetting where hand-annotated question entities are supplied to the model,leaving the important and non-trivial task of entity resolution (ER) outsidethe scope of E2E learning. In this work, we extend the boundaries of E2Elearning for KGQA to include the training of an ER component. Our model onlyneeds the question text and the answer entities to train, and delivers astand-alone QA model that does not require an additional ER component to besupplied during runtime. Our approach is fully differentiable, thanks to itsreliance on a recent method for building differentiable KGs (Cohen et al.,2020). We evaluate our E2E trained model on two public datasets and show thatit comes close to baseline models that use hand-annotated entities.

End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs