# Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian
## 3 Method
### Corpus
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**FT**} & \multirow{2}{*}{**PP**} & \multicolumn{2}{c}{**M(SD) FT**} & \multicolumn{2}{c}{**M(SD) FT**} & \multicolumn{2}{c}{**M(SD) FT**} \\  & & & **Words** & **Unique Words** & **TTR** \\ \hline [PERSON] & 96 & 647 & 1629.16 (1341.48) & 735.19 (462.04) & 0.5110 (0.0844) \\ [PERSON] & 132 & 485 & 808.17 (720.04) & 392.20 (244.57) & 0.5256 (0.0660) \\
**[PERSON]** & & & & \\
**[PERSON]** & 47 & 747 & 4015.40 (2224.96) & 1391.72 (658.60) & 0.3730 (0.0599) \\ [PERSON] & 24 & 343 & 3482.62 (2253.38) & 1236.46 (694.14) & 0.4027 (0.0883) \\
**[PERSON]** & 55 & 353 & 1533.58 (1582.43) & 609.09 (449.03) & 0.4649 (0.0767) \\
**[PERSON]** & 41 & 382 & 2176.71 (1705.21) & 792.00 (454.83) & 0.4091 (0.0702) \\ [PERSON] & 46 & 331 & 1689.11 (1246.86) & 711.00 (412.92) & 0.4728 (0.0815) \\
**[PERSON]** & 89 & 1716 & 4692.76 (2156.69) & 1306.64 (485.87) & 0.3043 (0.0665) \\
**[PERSON]** & 45 & 424 & 2291.13 (232.91) & 720.96 (554.58) & 0.4420 (0.1537) \\
**[PERSON]** & 60 & 585 & 2444.30 (1541.96) & 895.13 (466.55) & 0.3832 (0.0485) \\
**[PERSON]** & 59 & 619 & 2544.49 (1770.39) & 969.80 (518.88) & 0.4165 (0.0654) \\
**[PERSON]** & 27 & 405 & 3642.78 (2167.54) & 1284.67 (674.06) & 0.3834 (0.0767) \\ [PERSON] & 32 & 68 & 409.62 (394.16) & 216.28 (174.42) & 0.5938 (0.1093) \\ [PERSON] & 46 & 658 & 3478.37 (1826.51) & 1234.85 (472.30) & 0.3803 (0.0532) \\
**[PERSON]** & 35 & 375 & 2606.57 (1701.70) & 998.20 (540.52) & 0.4173 (0.0781) \\ [PERSON] & 306 & 2982 & 2437.67 (2215.16) & 970.28 (741.50) & 0.4834 (0.1054) \\
**[PERSON]** & 20 & 499 & 6299.85 (1202.32) & 2177.75 (369.46) & 0.3494 (0.0240) \\
**[PERSON]** & 40 & 630 & 3768.72 (1614.16) & 1126.40 (359.51) & 0.3183 (0.0517) \\ [PERSON] & 63 & 267 & 976.13 (581.40) & 472.32 (234.24) & 0.5279 (0.0845) \\ \hline Aggregate & 1263 & 12,516 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset overview (authors marked in bold are also included in the subset with 10 authors). Figure 2: Word distribution for FT versus PP. The dataset incorporates a diverse range of authors, each contributing with different numbers of texts and paragraphs. We can identify patterns and trends in their writing styles, vocabulary usage, and text lengths by comparing statistical measures across authors. To understand the statistical differences between the authors, we further explore surface metrics like the number of words, unique words, or lexical diversity (TTR score). The number of words indicates the average length of texts authored by each writer--i.e., authors with higher mean values produce longer texts on average. Additionally, examining the standard deviation values enables us to understand the variability in text length within each author's work. The unique words feature measures the richness of vocabulary used by each author. Authors with higher mean values for unique words tend to use a more diverse range of vocabulary in their writing. Comparing the mean and standard deviation values for unique words can reveal authors who consistently employ a wide range of vocabulary versus those who exhibit less variability in their word choices. The type-token ratio (TTR) quantifies the lexical diversity of a text by calculating the ratio of unique words to total words. A higher TTR indicates greater lexical diversity, while a lower TTR suggests repetitive word usage. Authors with consistently high TTR values may exhibit more varied and sophisticated language use, while those with lower TTR values may prefer simpler or more repetitive language. Discussing potential biases within our dataset and their impact on the model's predictions is essential. Our dataset exhibits an important degree of imbalance in the distribution of texts, with certain authors being disproportionately represented. For instance, we observe in the PP set 2982 paragraphs for [PERSON] compared to only 68 paragraphs for [PERSON]. Similarly, there are only 20 stories from [PERSON] compared to 132 from [PERSON] in the FT set.