Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment
Table 4: Hit@1 accuracies and decoding rounds on the development set for different α values. The first row lists the accuracies of the GM baseline.
['[ITALIC] α', 'Decoding Rounds', '[ITALIC] ZH-EN', '[ITALIC] JA-EN', '[ITALIC] FR-EN']
We also investigated the impact of the probability threshold α on the performance for our Easy-to-Hard decoding method. hit@1 accuracies on these datasets. We can see that our model could benefit from decreasing α until it reaches 0.75. It is expected to find that lower α may hurt the performance since it incorporates some incorrect predictions as easy (gold) alignments into the model. Recall that in our decoding algorithm, we continuously perform the inference until less than K new easy alignments are found in the previous round. To better understand why more converge rounds are required, we analyzed the intermediate established alignments during the inference. We find this is due to those incorrect alignments introduced by reducing α produce a chain reaction, which offers the model more confidence about some uncertain but incorrect alignments, resulting in more decoding rounds.