  We present two novel datasets for the low-resource language Vietnamese to
assess models of semantic similarity: ViCon comprises pairs of synonyms and
antonyms across word classes, thus offering data to distinguish between
similarity and dissimilarity. ViSim-400 provides degrees of similarity across
five semantic relations, as rated by human judges. The two datasets are
verified through standard co-occurrence and neural network models, showing
results comparable to the respective English datasets.
