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arXiv · 2104.01569

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

Endri Kacupaj·Joan Plepi·Kuldeep Singh·Harsh Thakkar·Jens Lehmann·Maria Maleshkova
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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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Endri Kacupaj, Joan Plepi, Kuldeep Singh, Harsh Thakkar, Jens Lehmann, Maria Maleshkova. 2021-04-04. Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks. https://arxiv.org/abs/2104.01569

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