Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing
Table 2: Ablation study: average UAS of baseline model with different sets of constraints. Proj. represent projective constraints. C1-C3 and oracle are introduced in 4.1. The improvements for projective constraint and oracle are compared to baseline. For the other three constraint sets the improvement is compared to model with projective constraint.
['Model', 'UAS', 'coverage', 'Δ']
Here we use the oracle setting to reduce the noise introduced by corpus-statistics estimation errors. The results are based on Lagrangian relaxation inference. All the constraints we formulated have positive contributions to the performance improvements. C1=(NOUN) brings the largest gain probably because its widest coverage.