Relational Word Embeddings
Table 5: Pearson (r) and Spearman (ρ) correlation on a subset of the HyperLex lexical split. Models marked with † use external resources. All models concatenate their encoded representations with the baseline vector difference of standard FastText word embeddings.
['Encoding', 'Model', '[ITALIC] r', '[ITALIC] ρ']
Even though the results are low overall (noting e.g. that results for the random split are in some cases above 50% as reported in the literature), our model can clearly generalize better than other models. Interestingly, methods such as Retrofitting and Attract-Repel perform worse than the FastText vectors. This can be attributed to the fact that these models have been mainly tuned towards similarity, which is a feature that loses relevance in this setting. Likewise, the relation-based embeddings of Pair2Vec do not help, probably due to the high-capacity of their model, which makes the word embeddings less informative.