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In this paper, we are interested in developing semantic parsers whichunderstand natural language questions embedded in a conversation with a userand ground them to formal queries over definitions in a general purposeknowledge graph (KG) with very large vocabularies (covering thousands ofconcept names and relations, and millions of entities). To this end, we developa dataset where user questions are annotated with Sparql parses and systemanswers correspond to execution results thereof. We present two differentsemantic parsing approaches and highlight the challenges of the task: dealingwith large vocabularies, modelling conversation context, predicting querieswith multiple entities, and generalising to new questions at test time. We hopeour dataset will serve as useful testbed for the development of conversationalsemantic parsers. Our dataset and models are released athttps://github.com/EdinburghNLP/SPICE.

Semantic Parsing for Conversational Question Answering over Knowledge Graphs