In the stage of error detection, the model only outputs the types of grammatical errors so that the tag vocabulary size is significantly reduced compared with other string editing based models.
To explore the potential of edges, EAGM learns edge attention on the assignment graph to 1) reveal the impact of each edge on graph matching, as well as 2) adjust the learning of edge representations adaptively.
Ranked #10 on Graph Matching on PASCAL VOC (matching accuracy metric)
A dual relation propagation approach is proposed, where relations captured by the generated graph are separately propagated from the seen and unseen subgraphs.
Graph convolutional neural networks (GCNNs) have been attracting increasing research attention due to its great potential in inference over graph structures.