# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 5 Discussion
### Trends in Misclassifications  
In this section, we investigate the trends related to misclassifications observed in our best-performing model (i.e., the hybrid Transformer) with the aim of enhancing our understanding of the limitations and challenges encountered by the model. Our objective is to reveal patterns within these misclassifications, identifying correlations between the types of errors produced by the model and the distinctive linguistic features associated with each author (presented in Table 13). To conduct this analysis, we refer to the confusion matrices (see Figures 4 and 5) that provide insights into the distribution of misclassifications across different authors and categories. Analyzing the FT 10-author set, we observe three misclassifications. [PERSON], for instance, was misclassified once with [PERSON]. The features associated with these errors include similar comma usage and third-person pronoun frequency. [PERSON] encountered one misclassification as [PERSON]; however, the linguistic analysis did not include common characteristics in their top three discriminative features. Lastly, [PERSON] was misclassified as [PERSON], and similar to the previous misclassification, correlating the two authors' profiles, there is no overlap in the top features of their writing style. From this analysis, it appears that misclassifications occur even when the top features of the misclassified authors do not overlap. This suggests that the misclassification is not attributed to specific linguistic features but rather contextual similarities. Further analysis extends to the PP 10-author set, uncovering additional misclassification patterns. [PERSON] faced the most misclassification, being mistaken three times for [PERSON], eight times for [PERSON], and three times for [PERSON]. Features correlated with these errors comprise disjunction, third-person pronoun usage, and sentence-level syntactic dependency frequencies. [PERSON], for instance, was misclassified twice, and key features associated with these errors include third-person pronoun, coordination dependency, and named entity frequencies. [PERSON] experienced two misclassifications as [PERSON] due to linguistic features like connector disjunction, coordination dependency, and connector frequencies. [PERSON] was misclassified as [PERSON], with common features comprising connector and sentence-level syntactic dependencies, and as [PERSON], with similar writing at the sentence dependencies level. [PERSON] was also misclassified as [PERSON], and [PERSON], due to features reflecting parse depth or punctuation usage. Furthermore, [PERSON] was misclassified as [PERSON], with top features comprising punctuation frequency, named entity recognition, and third-person pronoun usage. Lastly, [PERSON] experienced misclassifications, being mistaken for [PERSON] or for [PERSON] and once for [PERSON]. The linguistic features correlated with these errors involve third-person pronoun frequency, comma usage, and connector disjunction frequency. Based on the information presented in this section, several patterns in misclassifications can be identified. Certain authors, such as [PERSON] and [PERSON] or [PERSON] and [PERSON], are confused with each other, implying contextual similarities or thematic overlaps. Moreover, common linguistic features such as comma usage, third-person pronoun frequency, and syntactic dependencies are consistently associated with misclassifications across different authors. This underscores the complexity of author attribution, especially in distinguishing between authors with similar writing styles or  
Figure 4: Hybrid Transformer: confusion matrix for FT 10-authors. thematic content. Despite the robustness of the hybrid model, certain authors consistently display misclassifications, suggesting the presence of confounding factors. Hence, there emerges a clear need for the continuous refinement of the methodologies, with a focus on feature selection and model adaptation to better accommodate individual author styles and minimize misclassifications.