# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 6 Conclusions  
The current study contributes to the field of authorship attribution in the context of Romanian, a less-resourced language. We have released an open-source corpus representing an extension of the existing ROST dataset consisting of Romanian stories, providing valuable resources for further research and development in this area. Moreover, we conducted an exploration of existing methodologies and their limitations for authorship attribution, setting the context for the proposed solutions. Figure 5: Hybrid Transformer: confusion matrix for PP 10 authors. One of the key contributions of our study is the introduction of a hybrid Transformer model tailored for Romanian authorship attribution, which is evaluated against a baseline ensemble of seven machine learning classifiers leveraging majority soft voting. The hybrid Transformer incorporates both numerical features from linguistic ReaderBench indices and textual features from a BERT encoder for author prediction. Furthermore, we consider feature selection via Kruskal-Wallis's non-parametric statistical test, enhancing the model's capability to identify relevant features for prediction. This methodology represents an innovative contribution to the field, offering a more refined and efficient approach to feature selection in authorship attribution tasks. Additionally, the proposed baseline ensemble model effectively improves the predictive performance compared to traditional standalone models or other existing ML methods. Furthermore, we compare the performance of our proposed models with similar existing methods. Results argue that our models consistently outperform or match the performance of existing approaches across various datasets and input representations. Our hybrid RoBERT model achieved an F1-score of 0.95 and an error rate of 0.04 on the dataset with 10 authors when considering paragraphs as input. Additionally, when considering the dataset with 19 authors, our hybrid Transformer method reached an F1 score of 0.87 and an error rate of 0.12 on full-text data. These results underpin the effectiveness of our proposed method, outperforming the baseline ensemble learning approach, which achieved an F1-score of 0.82 with an error rate of 0.14 on the 10-author dataset and an F1-score of 0.84 with an error rate of 0.15 on the 19-author dataset. In addition, the author's writing styles were defined based on the most discriminative features in their texts, cross-correlated with lexical statistics. Our analysis provided insights into the linguistic patterns employed by the selected Romanian authors. Moreover, we conducted a linguistic analysis leveraging textual complexity indices and leveraged [PERSON]'s and [PERSON]'s Q statistical tests to evaluate the performance evolution across the best three models while also highlighting patterns in misclassifications. In this paper, we focused on the challenges of authorship attribution in the context of Romanian texts, highlighting the unique linguistic complexities that pose problems to conventional solutions. While our discussion has primarily centered on developing methodologies for the Romanian language, we acknowledge the broader implications of our research beyond the immediate scope of our study. By addressing the problem of authorship attribution in Romanian, our work may contribute to a deeper understanding of computational linguistics and text analysis in multilingual environments. Moreover, the methods presented in this paper have diverse applications in diverse research areas. For instance, our methods can assist in analyzing disputed documents and legal texts in forensic linguistics. Additionally, our research may be used in historical document analysis to authenticate and attribute authorship to archival materials. Additionally, our research can enhance the exploration of authorial style in literary studies. Based on these potential applications, we underpin the significance of our research and its potential to extend beyond the limits of our immediate research context.