CRF Autoencoder for Unsupervised Dependency Parsing\ThanksThis work was supported by the National Natural Science Foundation of China (61503248).
Table 4: Parsing accuracy on seven languages. Our model is compared with DMV (Klein and Manning, 2004), Neural DMV (Jiang et al., 2016), and Convex-MST (Grave and Elhadad, 2015)
['[EMPTY]', 'Basque', 'Czech', 'Danish', 'Dutch', 'Portuguese', 'Slovene', 'Swedish', 'Avg']
We did not use the Arabic corpus because the number of training sentences with length ≤ 10 is less than 1000. The accuracies of DMV and Neural DMV are from Jiang et.al Jiang et al. Both our model (CRFAE) and Convex-MST were tuned on the validation set of each corpus. It can be seen that our method achieves the best results on average. Besides, our method outperforms Convex-MST both with and without linguistic prior. From the results we can also see that utilizing universal linguistic prior greatly improves the performance of Convex-MST and our model.