QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
458 Citations•2021•
Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut
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This work proposes a new model, QA-GNN, which addresses the problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) through two key innovations: relevance scoring and joint reasoning.
Abstract
Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, Jure Leskovec. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.