Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition\ThanksThis work was supported by the National Natural Science Foundation of China (61503248).
Table 1: Directed dependency accuracy on thirty datasets with test sentences of length ≤ 40. The last row indicates the average directed accuracy on sentences of length ≤ 15. M (Convex-MST) and D (LC-DMV) are the independently trained baselines. D-I is the third baseline in which the LC-DMV training is initialized by the parses produced from the trained Convex-MST model. With our jointly trained models, M-J and D-J denote separate decoding and DD denotes joint decoding.
['Language', 'M', 'D', 'D-I', 'M-J', 'D-J', 'DD']
We can see that with joint training and independent decoding, LC-DMV and Convex-MST can achieve superior overall performance than when they are separately trained with or without mutual initialization. Joint decoding with our jointly trained models performs worse than independent decoding. We made the same observation when applying joint decoding to the separately trained models (not shown in the table). We believe this is because unsupervised parsers have relatively low accuracy and forcing them to reconcile would not lead to better parses. On the other hand, joint decoding during training helps propagate useful inductive biases between models and thus leads to better trained models.