Multilingual Knowledge Graph Embeddings forCross-lingual Knowledge Alignment
Table 5: Accuracy of TWA verification (%).
['Data Set Languages', 'WK3l-15k En&Fr', 'WK3l-15k En&De', 'WK3l-120k En&Fr', 'WK3l-120k En&De']
Results. Thus, the results are statistically sufficient to reflect the performance of classifiers. Note that the results appear to be better than those of the previous task since this is a binary classification problem. Intuitively, the linear-transformation-based MTransE perform steadily and take the lead on all settings. We also observe that Var5, though learns an additional relation-dedicated transformation, still performs considerably close to Var4 (the difference is at most 0.85%). The simple Var1 is the runner-up, and is between 1.65% and 3.79% to the optimal solutions. However the relation-dedicated calibration in Var2 causes a notable setback (4.12%∼8.44% from the optimal). The performance of Var3 falls behind slightly more than Var2 (4.52%∼10.79% from the optimal) due to the failure in distinguishing cross-lingual alignment from regular relations. Meanwhile, we single out the accuracy on the portion of negative cases where only the relation is corrupted for English-French in WK3l-15k. The five variants receive 97.73%, 93.78%, 82.34%, 98.57%, and 98.54%, respectively. The close accuracy of Var4 and Var5 indicates that the only transformation learnt from entities in Var4 is enough to substitute the relation-dedicated transformation in Var5 for discriminating relation alignment, while learning the additional transformation in Var5 does not notably interfere the original one. However, it applies differently to axis calibration since Var2 does not improve but actually impairs the cross-lingual transitions for relations. For the same reasons as above, LM and CCA do not match with MTransE in this experiment as well, while OT performs closely to some variants of MTransE, but is still left behind by Var4 and Var5. Results. They imply that MTransE preserves well the characterization of monolingual knowledge. For each setting, Var1, Var4, and Var5 perform at least as well as TransE, and some even outperforms TransE under certain settings. This signifies that the alignment model does not interfere much with the knowledge model in characterizing monolingual relations, but might have actually strengthened it since coherent portions of knowledge are unified by the alignment model. Since such coherence is currently not measured, this question is left as a future work.