Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging
Table 3: Ablation study, Token level accuracy (in %) when progressively ablating PretRand components.
['[BOLD] Method', '[BOLD] TPoS', '[BOLD] ArK', '[BOLD] TweeBank', '[BOLD] Avg']
To assess the contribution of different components of PretRand, we performed an ablation study. Specifically, we successively ablated the main components of PretRand, namely, the learnable vectors (learnVect), the longer training for random units (random++) and the normalisation (ℓ2-norm). More importantly, adding each of them successively, makes the performances significantly better, which highlights the importance of every component.