Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment
Table 2: Evaluation results on the datasets.
['Method', '[ITALIC] ZH- [ITALIC] EN', '[ITALIC] JA- [ITALIC] EN', '[ITALIC] FR- [ITALIC] EN']
When these two methods are combined, the overall performance is further improved, outperforming previous works. We also investigate whether our proposed reasoning methods could also boost existing baselines. Recall that, the Easy-to-Hard decoding method requires an enhanced alignment model that could integrate the easy alignment information. Since designing enhanced versions for these baselines is beyond our goal, here we only enforce that that the aligned entities found in the easy alignments have the same surface form. We find that this simplified strategy could still improve these baselines, which also suggests that our proposed decoding strategy is generally helpful to the alignment models.