A Common Semantic Space for Monolingual and Cross-Lingual Meta-Embeddings
Table 1: Coverage in the word similarity datasets
['Embedding', 'Cov. All', 'Cov. Rel', 'Cov. Sim']
UKB presents the lower coverage while GloVe and FastText exhibit almost full coverage. Multiple approaches have been applied to deal with the Out-of-Vocabulary problem (OOV). For instance, in order to assign an arbitrary vector for a missing word, previous works assign a predefined similarity (i.e. 0.5) or, alternatively, do not take into account the pairs of words when there is no representation for at least one of the embeddings. For a fairer comparison, we use the latter option since assigning arbitrary values cause embeddings with larger vocabularies (i.e. our own meta-embeddings) to obtain better results.