  Existing KBQA methods have traditionally relied on multi-stage methodologies,
involving tasks such as entity linking, subgraph retrieval and query structure
generation. However, multi-stage approaches are dependent on the accuracy of
preceding steps, leading to cascading errors and increased inference time.
Although a few studies have explored the use of end-to-end models, they often
suffer from lower accuracy and generate inoperative query that is not supported
by the underlying data. Furthermore, most prior approaches are limited to the
static training data, potentially overlooking the evolving nature of knowledge
bases over time. To address these challenges, we present a novel end-to-end
natural language to SPARQL framework, SPARKLE. Notably SPARKLE leverages the
structure of knowledge base directly during the decoding, effectively
integrating knowledge into the query generation. Our study reveals that simply
referencing knowledge base during inference significantly reduces the
occurrence of inexecutable query generations. SPARKLE achieves new
state-of-the-art results on SimpleQuestions-Wiki and highest F1 score on LCQuAD
1.0 (among models not using gold entities), while getting slightly lower result
on the WebQSP dataset. Finally, we demonstrate SPARKLE's fast inference speed
and its ability to adapt when the knowledge base differs between the training
and inference stages.
