# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 4 Results
These results underscore the relevance of leveraging hybrid features to enhance \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Corpus** & **Input** & **Top Features** & **Model** & **F1** & **Error** \\ \hline \multirow{8}{*}{PP} & \multirow{8}{*}{RBI} & \multirow{8}{*}{KW Top 500} & Decision Trees & 0.36 & 0.63 \\  & & & Extra Trees & 0.44 & 0.47 \\  & & Logistic Regression & 0.61 & 0.37 \\  & & MLP & 0.58 & 0.40 \\  & & Random Forest & 0.46 & 0.46 \\  & & SVM & 0.60 & 0.39 \\  & & XGBoost & 0.56 & 0.40 \\  & & **Ensemble Learning** & **0.69** & **0.28** \\  & & RoBERT & 0.73 & 0.22 \\  & & **Hybrid RoBERT** & **0.77** & **0.23** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The 19-author paragraphs (PP) results (bold represents the best results from each category). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Corpus** & **Input** & **Top Features** & **Model** & **F1** & **Error** \\ \hline \multirow{8}{*}{FT} & \multirow{8}{*}{RBI} & Decision Trees & 0.51 & 0.48 \\  & & Extra Trees & 0.62 & 0.37 \\  & & Logistic Regression & 0.74 & 0.26 \\  & & MLP & 0.66 & 0.34 \\  & & Random Forest & 0.65 & 0.35 \\  & & SVM & 0.72 & 0.28 \\  & & XGBoost & 0.74 & 0.26 \\  & & **Ensemble Learning** & **0.78** & **0.20** \\  & & RoBERT & 0.94 & 0.05 \\  & BERT Embeddings + RBI & KW Top 100 & **Hybrid RoBERT** & **0.95** & **0.04** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The 10-author paragraphs (PP) results (bold represents the best results from each category). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Corpus** & **Input** & **Top Features** & **Model** & **F1** & **Error** \\ \hline \multirow{8}{*}{PP} & \multirow{8}{*}{RBI} & Decision Trees & 0.51 & 0.48 \\  & & Extra Trees & 0.62 & 0.37 \\  & & Logistic Regression & 0.74 & 0.26 \\  & & MLP & 0.66 & 0.34 \\  & & Random Forest & 0.65 & 0.35 \\  & & SVM & 0.72 & 0.28 \\  & & XGBoost & 0.74 & 0.26 \\  & & **Ensemble Learning** & **0.78** & **0.20** \\  & BERT Embeddings & RoBERT & 0.94 & 0.05 \\  & BERT Embeddings + RBI & KW Top 100 & **Hybrid RoBERT** & **0.95** & **0.04** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The 10-author paragraphs (PP) results (bold represents the best results from each category). authorship attribution accuracy, particularly in datasets with few authors. Furthermore, standalone models exhibited varying performance levels. LR and XGB achieved the highest F1 scores of 0.74, followed closely by SVM, with an F1 score of 0.72. However, ensemble learning outperformed all standalone models with an F1 score of 0.78, highlighting its efficacy in aggregating predictions from diverse models. We further compare the best three models on the 10-author set, namely, ensemble learning (EL), the BERT-based, and the hybrid Transformer models. We choose to present the results on the subset of 10 authors for an easier correlation with prior existing methods in the field, linguistic analysis, and trends in misclassifications, which are analyzed in the next section. First, we consider the performance of the three models on both the FT and PP 10-author test sets (see Table 9). Second, we conduct [PERSON]'s Q test [56] to determine whether there is a significant difference in proportions among our models' performance. [PERSON]'s Q test helps assess whether there is a consistent pattern of differences in proportions across multiple groups (i.e., prediction models). The test compares the proportion of successes or positive outcomes (i.e., correct predictions) within each group, considering the repeated measures or dependent nature of the data. A significant result (i.e., a small p-value) from [PERSON]'s Q test suggests that at least one of the groups differs significantly from the others in terms of the proportion of successes. Third, we leverage pairwise McNemar's Test [57] to compare the performance of the three best models (i.e., ensemble-based, BERT- based, and hybrid Transformer) in our multiclass classification scenario.