Plepi, J.J.PlepiKacupaj, E.E.KacupajSingh, KuldeepKuldeepSinghThakkar, HarshHarshThakkarLehmann, JensJensLehmann2022-03-152022-03-152021https://publica.fraunhofer.de/handle/publica/41271310.1007/978-3-030-77385-4_21Neural 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 (Context trAnsformeR sTacked pOinter Networks), 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.en005006629conference paper