A Dynamic Window Neural Network for CCG Supertagging
Table 2: 1-best supertagging accuracy on CCGbank. “no dyn” refers to the models that do not use the filter gates to concatenate the tokens in a context window, “no char” refers to the models that do not use the character-level information, “drop emb” refers to dropout on word embeddings rather than on dynamic filters.
['Model', 'Dev', 'Test']
We report the highest 1-best supertagging accuracy on the development set for final testing. We notice that stacked bi-LSTM (with depth 2) performs the best than other neural based models. Our dynamic window approach provides the highest (+11%) relative performance gain. The character-level information (+ 6% relative accuracy) and dropout (+ 8% relative accuracy) are also helpful to improve the performance. We observe that dropout on words is superior to dropout on word embeddings.