  In this paper, we are interested in developing semantic parsers which
understand natural language questions embedded in a conversation with a user
and ground them to formal queries over definitions in a general purpose
knowledge graph (KG) with very large vocabularies (covering thousands of
concept names and relations, and millions of entities). To this end, we develop
a dataset where user questions are annotated with Sparql parses and system
answers correspond to execution results thereof. We present two different
semantic parsing approaches and highlight the challenges of the task: dealing
with large vocabularies, modelling conversation context, predicting queries
with multiple entities, and generalising to new questions at test time. We hope
our dataset will serve as useful testbed for the development of conversational
semantic parsers. Our dataset and models are released at
https://github.com/EdinburghNLP/SPICE.
