  This paper provides a comparative analysis of the performance of four
state-of-the-art distributional semantic models (DSMs) over 11 languages,
contrasting the native language-specific models with the use of machine
translation over English-based DSMs. The experimental results show that there
is a significant improvement (average of 16.7% for the Spearman correlation) by
using state-of-the-art machine translation approaches. The results also show
that the benefit of using the most informative corpus outweighs the possible
errors introduced by the machine translation. For all languages, the
combination of machine translation over the Word2Vec English distributional
model provided the best results consistently (average Spearman correlation of
0.68).
