  Neural Machine Translation (NMT) models from English to SPARQL are a
promising development for SPARQL query generation. However, current
architectures are unable to integrate the knowledge base (KB) schema and handle
questions on knowledge resources, classes, and properties unseen during
training, rendering them unusable outside the scope of topics covered in the
training set. Inspired by the performance gains in natural language processing
tasks, we propose to integrate a copy mechanism for neural SPARQL query
generation as a way to tackle this issue. We illustrate our proposal by adding
a copy layer and a dynamic knowledge base vocabulary to two Seq2Seq
architectures (CNNs and Transformers). This layer makes the models copy KB
elements directly from the questions, instead of generating them. We evaluate
our approach on state-of-the-art datasets, including datasets referencing
unknown KB elements and measure the accuracy of the copy-augmented
architectures. Our results show a considerable increase in performance on all
datasets compared to non-copy architectures.
