Improving Neural Parsing by Disentangling Model Combination and Reranking Effects
Table 3: Test F1 scores on section 23 of the PTB, by treebank training data conditions: either using only the training sections of the PTB, or using additional silver data (+S).
['Model', 'PTB', '+S']
Given the success of model combination between the base model and a single generative model, we also investigate the hypothesis that the generative models are complementary. The same trends we observed on the development data, on which the interpolation parameters were tuned, hold here: score combination improves results for all models (row 3 vs. row 2; row 6 vs. row 5), with candidate augmentation from the generative models giving a further increase (rows 4 and 7). For our experiments we train RD and RG using the same silver dataset. As in the PTB training data setting, using all models for candidates and score combinations is best, achieving 94.66 F1 (row 9).