Learning Constraints for Structured Prediction Using Rectifier Networks
Table 4: Token level accuracies (in percentage) for the chunking baseline and constrained model. The results are shown on n-gram Label Existence and n-gram Part of Speech constraints with n={2,3} and n={3,4} respectively. The results are shown on {1%,5%,10%,25%,50%,100%} of training data. Exact decoding with Viterbi algorithm and Search w/o constraint are baseline models which do not incorporate constraints during inference.
['[BOLD] Constraint', '[ITALIC] n', '[BOLD] Percentage of training data used  [BOLD] 1%', '[BOLD] Percentage of training data used  [BOLD] 5%', '[BOLD] Percentage of training data used  [BOLD] 10%', '[BOLD] Percentage of training data used  [BOLD] 25%', '[BOLD] Percentage of training data used  [BOLD] 50%', '[BOLD] Percentage of training data used  [BOLD] 100%']
We observe that the POS constraint improves the performance of the baseline models significantly, outperforming the beam search baseline on all training ratios. More importantly, the results show sizable improvements in accuracy for smaller training ratios (e.g, 4.41% and 5.23% improvements on exact and search baselines respectively with 1% training data ). When the training ratios get bigger, we expect the models to learn these properties and hence the impact of the constraints decreases.