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• W3094328377 abstract "Recently, knowledge graph (KG) augmented models have achieved noteworthy success on various commonsense reasoning tasks. However, KG edge (fact) sparsity and noisy edge extraction/generation often hinder models from obtaining useful knowledge to reason over. To address these issues, we propose a new KG-augmented model: Hybrid Graph Network (HGN). Unlike prior methods, HGN learns to jointly contextualize extracted and generated knowledge by reasoning over both within a unified graph structure. Given the task input context and an extracted KG subgraph, HGN is trained to generate embeddings for the subgraph's missing edges to form a hybrid graph, then reason over the hybrid graph while filtering out context-irrelevant edges. We demonstrate HGN's effectiveness through considerable performance gains across four commonsense reasoning benchmarks, plus a user study on edge validness and helpfulness." @default.
• W3094328377 created "2020-10-29" @default.
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• W3094328377 date "2020-10-24" @default.
• W3094328377 modified "2023-09-28" @default.
• W3094328377 title "Learning Contextualized Knowledge Structures for Commonsense Reasoning" @default.
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• W3094328377 doi "https://doi.org/10.48550/arxiv.2010.12873" @default.
• W3094328377 hasPublicationYear "2020" @default.
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