Co-training Embeddings of Knowledge Graphs and Entity Descriptionsfor Cross-lingual Entity Alignment
Table 3: Results of zero-shot alignment.
['Language Metric', 'En-Fr  [ITALIC] Hit@1', 'En-Fr  [ITALIC] Hit@10', 'En-Fr  [ITALIC] MRR', 'En-De  [ITALIC] Hit@1', 'En-De  [ITALIC] Hit@10', 'En-De  [ITALIC] MRR']
Results. This also indicates that AGRU is more competent for proposing ILLs in co-training based on unseen descriptions than others. As expected, co-training effectively leverages the zero-shot alignment with an increment of Hit@1 by 4.04% and 2.63%, as well as Hit@10 by 11.97% and 4.33% respectively on the two language settings. The results by GRU and AGRU-mono show that self-attention and multilingual word embeddings are vital to capture the cross-lingual semantic relatedness of descriptions from the word level. Failing to capture the sequence information, Single-layer and CNN are left behind.