Complex Structure Leads to Overfitting:A Structure Regularization Decoding Method for Natural Language Processing
Table 3: Numbers of sentences, overt and covert elements in training and test sets.
['English', 'train', '#{Sent} 38667', '#{Overt} 909114', '#{Covert} 57342']
We use the standard training, validation, and test splits to facilitate comparisons. Accuracy is measured with unlabeled attachment score for all overt words (UASo): the percentage of the overt words with the correct head. We are also concerned with the prediction accuracy for empty elements. To evaluate performance on empty nodes, we consider the correctness of empty edges. We report the percentage of the empty words in the right slot with correct head. The i-th slot in the sentence means that the position immediately after the i-th concrete word. So if we have a sentence with length n, we get n+1 slots.