# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 4 Results  
The results on the full dataset with 19 authors are presented in Tables 5 and 6. In the full text (FT) corpus, employing only RoBERT embeddings resulted in an F1 score of 0.85, while integrating BERT embeddings with linguistic features (hybrid RoBERT) exhibited the best performance with an F1 score of 0.87. Additionally, we observed that SVM achieved the highest F1 score of 0.83 among standalone models, while ensemble learning surpassed all standalone models with an F1 score of 0.84. Correspondingly, in the paragraphs (PP) analysis, leveraging RoBERT resulted in an F1 score of 0.73, while hybrid RoBERT achieved the highest performance with an F1 score of 0.77. Furthermore, LR achieved the highest F1 score of 0.61. However, ensemble learning outperformed all standalone models with an F1 score of 0.69, indicating the advantage of combining predictions from multiple models. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Corpus** & **Model** & **Best Hyperparameters** \\ \hline \multirow{8}{*}{FT} & MLP & activation: relu, alpha: 0.0001, hidden\_layer\_sizes: (200,100), max\_iter: 200 \\  & XGB & polz: 0.8 \\  & SVM & C: 0.1, degree: 2, gamma: scale, kernel: polynomial \\  & LR & C: 100, penalty: 11, solver: liblinear \\  & RF & max\_depth: None, max\_features: auto, min\_samples\_leaf: 1, min\_samples\_split: 10, n\_estimators: 100 \\  & DT & max\_depth: None, max\_features: auto, min\_samples\_leaf: 1, min\_samples\_split: 5 \\  & ET & max\_depth: None, max\_features: auto, min\_samples\_leaf: 1, min\_samples\_split: 2, n\_estimators: 200 \\ \hline \multirow{8}{*}{PP} & MLP & activation: relu, alpha: 0.001, hidden\_layer\_size: (100), max\_iter: 100 \\  & XGB & polz: 0.8 \\  & SVM & C: 0.1, gamma: scale, kernel: linear \\  & LR & C: 1; penalty: 11, solver: liblinear \\  & RF & max\_depth: None, max\_features: auto, min\_samples\_leaf: 1, min\_samples\_split: 2, \\  & m & \_estimators: 100 \\  & DT & max\_depth: 20, max\_features: auto, min\_samples\_leaf: 1, min\_samples\_split: 5 \\  & ET & max\_depth: 20, max\_features: auto, min\_samples\_leaf: 1, min\_samples\_split: 2, \\ \hline \hline \end{tabular}
\end{table} 
Table 4: GridSearch best parameters for ML models on the 19-author set. \begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Corpus** & **Input** & **Top Features** & **Model** & **F1** & **Error** \\ \hline \multirow{8}{*}{FT} & & Decision Trees & 0.50 & 0.49 \\  & & Extra Trees & 0.74 & 0.23 \\  & & Logistic Regression & 0.72 & 0.25 \\  & RBI & KW Top 100 & MLP & 0.66 & 0.34 \\  & & Random Forest & 0.69 & 0.28 \\  & & SVM & 0.83 & 0.17 \\  & & XGBoost & 0.80 & 0.18 \\  & & **Ensemble Learning** & **0.84** & **0.15** \\  & BERT Embeddings & & RoBERT & 0.85 & 0.14 \\  & BERT Embeddings + RBI & KW Top 100 & **Hybrid RoBERT** & **0.87** & **0.12** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The 19-author full text (FT) results (bold represents the best results from each category). The results for authorship attribution on the subset with 10 authors are presented in Tables 7 and 8. For the full text (FT) corpus, employing RoBERT embeddings alone resulted in a competitive F1 score of 0.88, while integrating BERT embeddings with linguistic features (i.e., hybrid RoBERT) exhibited the best performance with an F1 score of 0.94, showcasing the advantage of leveraging a hybrid approach. Furthermore, employing various standalone ML models yielded diverse performance outcomes. RF and XGB achieved the highest F1 score of 0.81, closely followed by MLP and Extra Trees, with an F1 score of 0.78 and 0.77. However, the ensemble learning technique surpassed all standalone models, achieving an F1 score of 0.82, indicating the effectiveness of combining multiple models for improved performance. Similarly, for the paragraph (PP) analysis, employing RoBERT embeddings alone achieved a superior F1 score of 0.94, while the integration of BERT embeddings with linguistic features (i.e., hybrid RoBERT) achieved the highest performance with an F1 score of 0.95.