# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 5 Discussion
### Comparison with Existing Methods  
To ensure a fair comparison, we exclusively assessed existing methodologies in Romanian studies on authorship attribution. Given the varying linguistic complexities and model usage across languages, we focused entirely on Romanian studies. To our knowledge, only two existing studies exist, which are also outlined in Section 2 of this paper. Focusing on datasets with 10 authors (see Table 12), we observe that our RoBERT model outperformed the existing approaches [22; 51] for both FT and PP corpora. Additionally, our hybrid RoBERT model, which incorporates RBI features, achieved the highest F1 score of 0.95 for the PP corpus, indicating the effectiveness of leveraging both textual and numerical features for AA tasks. Moreover, our standalone ML-based approach, leveraging the RBI features with Kruskal-Wallis feature selection, achieved competitive results compared to the existing methods. Our method outperformed the previous studies [17; 22] in terms of F1 score (0.81 versus 0.79) and error rate (0.18 versus 0.20) for the full text (FT) corpus. Moving on to ensemble learning methods, our approach achieved competitive performance compared to the existing ensemble approach [22] for both FT and PP corpora with 10 authors. Specifically, our ensemble learning method achieved an F1 score of 0.82 for the FT corpus and 0.78 for the PP corpus, arguing for the robustness of our approach in leveraging multiple classifiers to improve classification accuracy. Regarding the full dataset with 19 authors, our hybrid RoBERT model achieved the highest F1 score of 0.87 for the FT corpus, highlighting the strength of leveraging both textual and numerical features for AA tasks involving larger author sets. Furthermore, our standalone ML-based approach with RBI features again achieved competing performance compared to existing methods, achieving the highest F1 score of 0.84 for the FT corpus with 19 authors. Our results are consistent and surpass the performance reported by [PERSON] et al. [22], who included 20 authors for an English corpus. The authors leveraged DistilBERT and scored an F1 of 0.76, with an error rate of 0.23. The same study employed an ensemble learning model with TF-IDF as input features, scoring a maximum of 0.74 F1 and 0.25 error rate. In analyzing the results, it is noteworthy that the rank and performance of the models vary across different datasets and input representations. The rank, denoted by Roman \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test** & **Value** & \(\mathbf{p}\)**-Value** & **Adjusted \(\mathbf{p}\)**-Value** \\ \hline [PERSON]’s Q & 201.1 & \(<\)2.2 \(\times\) 10\({}^{-16}\) & - \\ \hline McNemar (EL–BERT) & - & 2.11 \(\times\) 10\({}^{-27}\) & 3.16 \(\times\) 10\({}^{-27}\) \\ McNemar (EL–Hybrid) & - & 1.22 \(\times\) 10\({}^{-28}\) & 3.66 \(\times\) 10\({}^{-28}\) \\ McNemar (BERT–Hybrid) & - & 0.5 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of Cochran’s Q test and pairwise McNemar tests for 10-author PP test set. numerals, reflects the comparative performance of each model in terms of F1 score and error rate. Across both dataset versions, with 10 authors and 19 authors, our proposed method consistently achieved either the first or second rank. Particularly, for the 10-author version, the top three approaches include standalone models using RBI features with KW top features, ensemble learning with BERT embeddings, and our proposed hybrid RoBERT method. These methods showcased competitive performance, with our hybrid RoBERT outperforming the standalone models and closely matching the performance of the ensemble learning approach. Similarly, in the dataset with 19 authors, our method using hybrid RoBERT combining BERT embeddings and RBI features was top-ranked. Overall, our results underpin the importance of feature selection and modeling techniques in enhancing AA performance. The choice of the feature selection method and model architecture can impact the outcome.