Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs

• URL: http://arxiv.org/abs/2103.07766v1
• Date: Sat, 13 Mar 2021 18:16:43 GMT
• Title: Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs
• Authors: Joan Plepi, Endri Kacupaj, Kuldeep Singh, Harsh Thakkar, Jens Lehmann
• Abstract summary: 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. We evaluate CARTON on a standard dataset for complex sequential question answering on which CARTON outperforms all baselines.
• Score: 4.6574525260746285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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