  Neural semantic parsing approaches have been widely used for Question
Answering (QA) systems over knowledge graphs. Such methods provide the
flexibility to handle QA datasets with complex queries and a large number of
entities. In this work, we propose a novel framework named CARTON, which
performs multi-task semantic parsing for handling the problem of conversational
question answering over a large-scale knowledge graph. Our framework consists
of a stack of pointer networks as an extension of a context transformer model
for parsing the input question and the dialog history. The framework generates
a sequence of actions that can be executed on the knowledge graph. We evaluate
CARTON on a standard dataset for complex sequential question answering on which
CARTON outperforms all baselines. Specifically, we observe performance
improvements in F1-score on eight out of ten question types compared to the
previous state of the art. For logical reasoning questions, an improvement of
11 absolute points is reached.
