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Graph Neural Networks with Generated Parameters for Relation Extraction

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Figure

Figure 1: An example of relation extraction from plain text. Given a sentence with several entities marked, wemodel the interaction between these entities by generating the weights of graph neural networks
Figure 2: Overall architecture: an encoding module takes a sequence of vector representations as inputs, and outputa transition matrix as output; a propagation module propagates the hidden states from nodes to its neighbourswith the generated transition matrix; a classification module provides task-related predictions according to nodesrepresentations.
Table 2: Results on human annotated dataset
Table 3: Results on distantly labeled test set
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