# Parallel Bidirectionally Pretrained Taggers as Feature Generators
## 4 Discussion  
From the results presented in Table 3 it can be seen that TreeTagger and spaCy gave similar scores, with TreeTagger having a slight edge in both the average performance (0.9542 and 0.9534 compared with 0.9455 and 0.9461) and stability scores (0.9961 and 0.9981 compared with 0.9964 and 0.9942), which aligns with our initial testing on this dataset. TreeTagger was trained using an additional lexical input in the form of annotated dictionaries with over two million word forms with their possible Part-of-Speech and lemma elements. Having dictionaries in the background, as expected, TreeTagger performed better than the others for unambiguous words that are in the dictionary, but spaCy was better at taking the context into account and resolving ambiguity. Another parallel was seen in the comparison of inversely-trained and standard taggers, where TreeTagger outperformed TreeTagger-I, and spaCy-I outperformed spaCy, both by a small margin of less than 0.1%. Stanza-based taggers matched this performance on several occasions (folds 1 and 4 for Stanza and folds 1 and 3 for Stanza-I), but they also displayed much greater instability with scores of 0.931 and 0.909, which subsequently resulted in less impressive averages of weighted \(f_{1}\) scores (around 0.9). Nevertheless, inversely-trained taggers performed similarly to standardly-trained ones, as shown in our initial tests for this experiment, which affirms that inverted taggers are independent systems with unique value, giving the answer to RQ1:  
**RQ1**: _Is bidirectional training of POS-taggers an appropriate way to create new independent models of unique value?_  
As for the composition-based taggers, their clear advantage over the standalone ones is visible in both Tables 4 and 5, as well as in Figure 5. This is in line with reports from other studies, where most of the combination methods outperformed all of the standalone taggers. The only exception in our experiment was _voting_ against TreeTagger and TreeTagger-I on folds 2, 3, and 4. The best overall \(f_{1}\) score achieved using a standalone tagger was 0.9556 (TreeTagger, fold 4), while the best result using composite methods was 0.9762 (bidirectional stacked classifier, fold 5), and the worst score achieved using the best performing composition-based method was 0.97. As is visible in Table 6, three of the four \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fold** & **Voting** & **Bidding** & **Scoring** & **Stacking** \\ \hline \#1 & 7.21\% & 29.59\% & 15.60\% & 13.48\% \\ \#2 & 8.12\% & 26.28\% & 20.12\% & 17.33\% \\ \#3 & 13.53\% & 30.77\% & 26.20\% & 13.68\% \\ \#4 & 19.32\% & 30.77\% & 10.53\% & 10.75\% \\ \#5 & 10.05\% & 33.42\% & 18.27\% & 16.49\% \\ \hline avg & 11.65\% & 30.17\% & 18.14\% & 14.35\% \\ min & 7.21\% & 26.28\% & 10.53\% & 10.75\% \\ max & 19.32\% & 33.42\% & 26.20\% & 17.33\% \\ stability & 0.08\% & \(-0.22\%\) & 0.31\% & 0.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Relative improvements achieved using the bidirectional architecture over the unidirectional one (upper section) and the relative average, minimum, and maximum values and the stability improvement (lower section). composition methods tested outperformed the baseline in all cases and in every category with an error reduction of up to 47.91% and 9.23% achieved for the worst case, giving a clear answer to RQ2:  
**RQ2**: _Can composition-based POS-taggers outperform the standalone pretrained taggers?_  
Our single-perceptron-based stacked classifier outperformed all other composition methods when it came to unidirectionally-built compositions, as is visible in Table 4. It was only matched by the _scoring_ method on fold 1, but it displayed the highest average scores, the highest minimum and maximum scores, as well as the highest stability, reducing the error rate of the baseline to an average of 37.47%, while the next best method (_scoring_) provided an average error reduction of 32.44%.