Mapping Unseen Words to Task-Trained Embedding Spaces
Table 3: Average Web Treebank development UAS at different threshold settings.
['Baseline', '[ITALIC]  [BOLD] t [BOLD] 1', '[ITALIC]  [BOLD] t [BOLD] 3', '[ITALIC]  [BOLD] t [BOLD] 5', '[ITALIC]  [BOLD] t [BOLD] ∞']
We see that t3 performs best, though settings t1 and t5 also improve over the baseline. At threshold t3 we have approximately 20,000 examples for training the mapper, while at threshold t5 we have only about 10,000 examples. We see a performance drop at t∞, so it appears better to directly use the task-specific embeddings for words that appear frequently in the training data. In other results reported in this paper, we used t3 for the Web Treebank test sets and t1 for the rest.