Deep Contextualized Self-training for Low Resource Dependency Parsing
Table 5: Tagging scheme error analysis.
['[BOLD] Model', 'AD-NC', 'AD-DR', 'AD-PDH', 'POS Head Error']
All in all, the DCST models outperform the Base parser across all comparisons, with DCST-ENS being the best model in all 8 cases except from one. The analysis indicates that in some cases a BiLSTM tagger with a given tagging scheme directly improves the capability of the final parser to capture the corresponding information. For example, DCST-DR, whose tagging scheme considers the distance of each word from the root of the tree, performs best (OntoNotes) or second best (UD) on the AD-DR metric compared to all other models except from the DCST-ENS model that contains the DCST-DR model as a component. Likewise, DCST-RPE, that encodes information about the POS tag of the head word for every word in the sentence, is the best performing model in terms of POS Head Error. In contrast to the relative success of DCST-RPE and DCST-DR in improving specific capabilities of the parser, DCST-NC, our weakest model across experimental setups, is also the weakest DCST model in this error analysis, even when considering the AD-NC metric that measures success in predicting the number of children a word has in the tree.