# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 2 Related Work on Authorship Attribution
### Authorship Attribution in Romanian  
Authorship attribution in less-resourced languages, such as Romanian, presents challenges due to the scarcity of available studies and limited datasets. While research in this area has primarily focused on well-resourced languages, such as English, the specific linguistic characteristics of Romanian texts remain relatively understudied.  
In a recent study by [PERSON] et al. [17], various Artificial Intelligence ML techniques were compared for the authorship classification of Romanian literary texts written by multiple authors, focusing on a limited number of speech parts (prepositions, adverbs, and conjunctions). The study introduced a new dataset of Romanian texts by different authors, incorporating diverse texts in terms of length, sources, or time periods. Three distinct feature lists were constructed based on the Inflexible Parts-of-Speech (IPoS), generating numerical representations of the texts. Five ML standalone models were employed, including Artificial Neural Networks (ANNs), Multi-Expression Programming (MEP), k-NN, SVM, and DT with C5.0. Experimental results indicated that the MEP method had the lowest overall error rate of 20.40%. Further analysis revealed that the dataset with prepositions and adverbs performed best. Several limitations were identified despite employing multiple methods. Determining the author of a text remained challenging, with only a few algorithms showcasing acceptable error rates on the test set. According to the authors, the dataset was also heterogeneous in various aspects, including text length, sources, time periods, and writing types, which may introduce complexity and variability into the analysis. Moreover, the choice to focus on a limited number of speech parts (prepositions, adverbs, and conjunctions) as features may overlook other relevant linguistic aspects that contribute to authorship attribution. While the MEP method achieved the best overall results with a 20.40% error rate, other methods' performance and generalizability to different datasets may vary, suggesting that the approach may not consistently achieve optimal results across all scenarios.  
A second study by [PERSON] [51] focused on authorship attribution using pre-trained language models, particularly BERT, to detect the authorship of Romanian texts. Similar to the previous study, this research used the same dataset, which is highly unbalanced in terms of the number of texts per author, source, time period, and writing type. Results showed a macro-accuracy exceeding 87%. Building upon previous work, the study leveraged shorter texts (200 tokens/words) for improved dataset balance. While previous methods achieved a maximum accuracy of 80.94%, BERT-based approaches yielded an 85.90% accuracy. However, a direct comparison with the previous method was challenging due to dataset modifications for the BERT processing requirements. Despite the promising results achieved with the BERT approach and shorter text segments, there were limitations to consider. While dividing texts into 200-word segments improves the dataset balance, it disregards the integrity of sentences. This approach may affect the context and semantic meaning of the text, which could influence the accuracy of authorship attribution. Therefore, further investigation is needed to assess the impact of maintaining sentence integrity and to understand the potential trade-offs between segmenting texts and preserving their original structure.