# Parallel Bidirectionally Pretrained Taggers as Feature Generators
## 4 Discussion
When it came to comparing bidirectionally-built composite methods (Table 5), _stacking_ was again the best performing method with an average error reduction of 46.51%, but the other methods improved slightly compared with the unidirectional architecture with _stacking_ providing an average error reduction of 44.72%. While this could be a sign of _stacking_ losing its improvement momentum with an increase in the number of taggers, it is more probable that it has a less steep slope and that it just performs better than _scoring_ when dealing with less information. This is supported by its high stability, with a score of 0.9978 across five folds. Nevertheless, considering its other advantages, such as the possibility of using an open tagset and diminishing inputs from poor performance taggers, together with the fact that it outperformed other composition-based tagging methods in both of our tests in terms of both performance and stability, _stacking_ was shown to be the best overall method (followed by _scoring_, and then _bidding_), thus answering RQ3:  
**RQ3**: _Is the stacked classifier the optimal way to combine outputs of pretrained POS-taggers in order to improve the overall performance and stability?_  
Relative improvements achieved through the use of the bidirectional architecture shown in Table 7 displayed substantial improvements over the unidirectional one. The bidirectional approach yielded improvements for all four tested methods in 100% of cases with average improvements ranging from 11.65% to 30.17%. It also resulted in increased minimum and maximum scores for all four methods and a stability improvement (up to 0.31%) for three out of the four tested methods. All of this undoubtedly answers RQ4:  
**RQ4**: _Is bidirectional training of POS-taggers through dataset augmentation an easy and appropriate way to further improve the results of the stacking architecture over standalone taggers?_  
After observing an increase in performance with an increased number of standalone taggers, we decided to try out one final run. As our dataset was annotated with two different tagsets, we took advantage of the possibility offered by the stacking environment to use them both. We trained another set of six standalone taggers on the second available tagset (SrpLemKor) and added the outputs of those taggers as features for the stacked classifier (increasing the number of features from 102 to 198). This final run resulted in an \(f_{1}\) score of 0.9783, which is a higher score than the maximum score of any previous runs (0.9762) and a clear indication that further improvements are possible. Considering this, future research will focus on a more detailed evaluation of the impact of training standalone models on additional tagsets (or other lexical information) and adding those models to the stacked architecture in pursuit of additional performance improvements. Something that could also benefit future research is the extension of the evaluation to other languages, where bidirectional training could have either a lesser or a higher impact. More detailed research of the impact of the dataset size and ratios of its splitting into training, tuning, and testing sets, as well as the impact of adding standalone taggers to, or removing them from, the composition could also result in a better understanding of the overall impact of the methodology for the task of POS-tagging as well as for other NLP tasks. Since the results obtained through this particular method are completely reliant on the standalone taggers used for the training of the stacked classifier, they can be applied for any language in which these standalone taggers are trained, making the approach language-independent. Furthermore, the use of the Universal POS tagset, which has corpora available for at least 122 languages ([[https://universaldependencies.org/](https://universaldependencies.org/)]([https://universaldependencies.org/](https://universaldependencies.org/)), accessed on 11 March 2022) makes the existing framework suitable for many other languages.