Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks

• URL: http://arxiv.org/abs/2104.01569v1
• Date: Sun, 4 Apr 2021 09:21:50 GMT
• Title: Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks
• Authors: Endri Kacupaj, Joan Plepi, Kuldeep Singh, Harsh Thakkar, Jens Lehmann, Maria Maleshkova
• Abstract summary: This paper addresses the task of (complex) conversational question answering over a knowledge graph. We propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEtworks) We show that LASAGNE improves the F1-score on eight out of ten question types.
• Score: 4.550927535779247
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses the task of (complex) conversational question answering over a knowledge graph. For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEtworks). It is the first approach, which employs a transformer architecture extended with Graph Attention Networks for multi-task neural semantic parsing. LASAGNE uses a transformer model for generating the base logical forms, while the Graph Attention model is used to exploit correlations between (entity) types and predicates to produce node representations. LASAGNE also includes a novel entity recognition module which detects, links, and ranks all relevant entities in the question context. We evaluate LASAGNE on a standard dataset for complex sequential question answering, on which it outperforms existing baseline averages on all question types. Specifically, we show that LASAGNE improves the F1-score on eight out of ten question types; in some cases, the increase in F1-score is more than 20% compared to the state of the art.

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