Arc-Standard Spinal Parsing with Stack-LSTMs
Table 1: Development results for spinal models, in terms of labeled precision (LP), recall (LR) and F1 for constituents, and unlabeled attachment score (UAS) against Stanford dependencies. Spinal models are trained using different head annotations (see text). Models labeled with “no n-comp” do not use node compositions. The model labeled with “dummy spines” corresponds to a standard dependency model.
['[EMPTY]', 'LR', 'LP', 'F1', 'UAS\xa0(SD)']
The model using rightmost heads works the best at 91.11 F1, followed by the one using leftmost heads. It is worth to note that the two models using structural head identities (right or left) work better than those using linguistic ones. This suggests that the Stack-LSTM model already finds useful head-child relations in a constituent by parsing from the left (or right) even if there are non-local interactions. In this case, head rules are not useful. The ablated models, tagged with “no n-comp”, perform from 0.5 to 1 points F1 worse, showing the benefit of adding constituent structure. Then, we check if constituent structure is any useful for dependency parsing metrics. To this end, we emulate a dependency parser using a spinal model by taking standard Stanford dependency trees and adding a dummy constituent for every head with all its children. This model, tagged “SD heads, dummy spines”, is slightly outperformed by the “SD heads” model using true spines, even though the margin is small.