On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning
Table 3: Spearman correlation performance of various cross-lingual word embedding models in the cross-lingual word similarity task.
['[BOLD] Wikipedia  [BOLD] Sup.', '[BOLD] Wikipedia  [BOLD] Model', '[BOLD] Wikipedia  [BOLD] EN-ES', '[BOLD] Wikipedia  [BOLD] EN-IT', '[BOLD] Wikipedia  [BOLD] EN-DE', '[BOLD] Wikipedia  [BOLD] EN-FA', '[BOLD] Wikipedia  [BOLD] Avg']
Given a pair of words from two different languages, the task of cross-lingual semantic word similarity consists in measuring to what extent both words are semantically similar. For the evaluation we make use of the cross-lingual word similarity datasets of the SemEval 2017 task [semeval2017similarity]. In this dataset each word from one language is paired with another word from the other language. This evaluation task has been found to correlate better with downstream performance than other intrinsic benchmarks [bakarov2018limitations]. The results are reported in terms of the Pearson and Spearman correlation with respect to human similarity judgments. The cross-lingual word similarity results for The languages available for this dataset are English, Spanish, Italian, German and Farsi, hence Finnish and Russian were not evaluated in this task.