Knowledge Graph Empowered Entity Description Generation
Table 2: Main results of models on ENT-DESC dataset. ↓ indicates lower is better.
['[BOLD] Models', '[BOLD] BLEU', '[BOLD] CHRF++', '[BOLD] METEOR', '[BOLD] TER [BOLD] ↓', '[BOLD] ROUGE1', '[BOLD] ROUGE2', '[BOLD] ROUGE [ITALIC] L']
Here, number of layers in all baseline models and our MGCN models are set to be 6 for fair comparison. Our models consistently outperform the baseline models on all evaluation metrics. S2S model has poor performance mainly because the structure of our input triples is complicated as explained earlier. Compared to GRN and GCN models, the BLEU score of MGCN model increases by 1.3 and 0.9 respectively. This result demonstrates the effectiveness of multi-graph transformation, which is able to capture more comprehensive information compared to Levi graph transformation, especially entity-to-entity information in the original graph. We then apply multiple methods of aggregation on top of multi-graph structure. MGCN+CNN and MGCN+SUM report the highest BLEU score 26.4, followed by MGCN+AVG. By applying our delexicalization technique, the results are further boosted by 3.2 to 3.6 BLEU score for both baseline and our proposed models.