Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs
Table 3: Results on WebNLG test set with seen categories.
['[BOLD] Model', '[BOLD] BLEU', '[BOLD] METEOR', '[BOLD] CHRF++', '[BOLD] #P']
CGE achieves a BLEU score of 62.30, 8.9% better than the best model of castro-ferreira- etal-2019-neural, who employ an end-to-end architecture based on GRUs. CGE using Levi graphs outperforms trisedya-etal-2018-gtr, an approach that encodes both intra-triple and inter-triple relationships, by 4.5 BLEU points. Interestingly, their intra-triple and inter-triple mechanisms are closely related with the local and global encodings. However, they rely on encoding entities based on sequences generated by traversal graph algorithms, whereas we explicitly exploit the graph structure, throughout the local neighborhood aggregation.