Viable Dependency Parsing as Sequence Labeling
Table 3: Performance of the PC2,800 model with UPoS- and XPoS-based encoding for each language on the dev set. # UPoS/XPoS represents the number of distinct UPoS/XPoS tags in the training set for each language.
['Treebank', 'UPoS-based UAS', 'UPoS-based LAS', 'UPoS-based # UPoS', 'XPoS-based UAS', 'XPoS-based LAS', 'XPoS-based # XPoS']
The results suggest that the performance of our system can be influenced by the size of the tag set. It appears that a very large tag set (for instance the XPoS tag set for Czech and Tamil) can hurt the performance of the model and significantly slow down the system, as it results into a large number of distinct labels for the sequence labeling model, increasing sparsity and making the classification harder. In case of Ancient Greek and Kazakh, the best performance is achieved with the XPoS-based encoding. In these corpora, the tag set is slightly bigger than the UPoS tag set. One can argue that the XPoS tags in this case were possibly more fine-grained and hence provided additional useful information to the system facilitating a correct label prediction, without being so large as to produce excessive sparsity.