Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs
This work addresses the problem of handling complex conversational queries over large-scale knowledge graphs for QA systems, representing an incremental advancement with specific gains.
The paper tackles conversational question answering over knowledge graphs by proposing CARTON, a multi-task semantic parsing framework that uses stacked pointer networks and a context transformer, resulting in performance improvements including an 11-point absolute gain in F1-score for logical reasoning questions.
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.