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