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• W3116238564 abstract "Dependency trees have been shown to be effective in capturing long-range relations between target entities. Nevertheless, how to selectively emphasize target-relevant information and remove irrelevant content from the tree is still an open problem. Existing approaches employing pre-defined rules to eliminate noise may not always yield optimal results due to the complexity and variability of natural language. In this paper, we present a novel architecture named Dynamically Pruned Graph Convolutional Network (DP-GCN), which learns to prune the dependency tree with rethinking in an end-to-end scheme. In each layer of DP-GCN, we employ a selection module to concentrate on nodes expressing the target relation by a set of binary gates, and then augment the pruned tree with a pruned semantic graph to ensure the connectivity. After that, we introduce a rethinking mechanism to guide and refine the pruning operation by feeding back the high-level learned features repeatedly. Extensive experimental results demonstrate that our model achieves impressive results compared to strong competitors." @default.
• W3116238564 created "2021-01-05" @default.
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• W3116238564 date "2020-01-01" @default.
• W3116238564 modified "2023-09-30" @default.
• W3116238564 title "Learning to Prune Dependency Trees with Rethinking for Neural Relation Extraction" @default.
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• W3116238564 doi "https://doi.org/10.18653/v1/2020.coling-main.341" @default.
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