As can be seen from the table, for both languages, adversarial training (AT) results in cleaner word embedding distributions than the baseline, with a higher cosine similarity within each POS cluster, and with a clear advantage in the average tightness across all the clusters.
the learned word vectors show stronger correlations with their POS tags.