Robust Multilingual Part-of-Speech Tagging via Adversarial Training
Table 1: POS tagging accuracy on the PTB-WSJ test set, with other top-performing systems.
['[BOLD] Model', '[BOLD] Accuracy']
As expected, our baseline (BiLSTM-CRF) model (accuracy 97.54%) performs on par with other state-of-the-art systems. Built upon this baseline, our adversarial training (AT) model reaches accuracy 97.58% thanks to its regularization power, outperforming recent POS taggers except \newcitewang:2015. The improvement over the baseline is statistically significant, with p-value < 0.05 on the t-test. We provide additional analysis on this result in later sections.