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 2: Average dependency length in the Convex-MST parses of the English test dataset.
['[BOLD] Methods', '[BOLD] Average Dependency Length']
The dependency length can be seen to decrease with joint training, showing the influence from LC-DMV. As to center embedding depth, we find that separately trained Convext-MST already produces very few center embeddings of depth 2 or more, so the influence from the center embedding constraint of LC-DMV during joint training is not obvious. We note that the influence on Convex-MST from LC-DMV during joint training is relatively small, which may contribute to the much smaller accuracy improvement (1.5%) of Convex-MST with joint training in comparison with the 9.3% improvement of LC-DMV. We conducted an additional experiment that scaled down the Convex-MST objective in joint training in order to increase the influence of LC-DMV. The results show that LC-DMV indeed influences Convex-MST to a greater degree, but the parsing accuracies of the two models decrease.