Robust Multilingual Part-of-Speech Tagging via Adversarial Training
Table 6: Cluster tightness evaluation for word embeddings, based on the cosine similarity measure. Higher scores indicate better clustering (cleaner word vector distribution). Each row corresponds to word vectors 1) at the beginning, 2) after baseline training, and 3) after adversarial training.
['POS Cluster', 'NN', 'VB', 'JJ', 'RB', 'Avg.']
We report the tightness scores for the four major clusters: noun, verb, adjective, and adverb (from left to right). 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. In other words, the learned word vectors show stronger correlations with their POS tags. This result confirms that training with adversarial examples can help to learn cleaner word embeddings so that the meaning / grammatical function of a word cannot be altered by a small perturbation in its embedding. This analysis provides a means to interpret the robustness to input perturbations, from the perspective of NLP.