## 5 Results  
In this section we present the results achieved on the datasets described in Section 3.2 and compare them with the models introduced in Section 4.1. If in Section 4.1 it is not stated otherwise, the results in the tables yielded by the models other than the 3 variants of MIs WiLDe (our proposed method) are cited from [PERSON] et al. [10]. The bold font and the underline stand for the best and second-best results, respectively. [PERSON] et al. report the results up to one place after the decimal point, which sometimes did not allow for a definite assertion as to which model performed better. As a consequence, there are columns with two values marked in bold or underlined. Using a horizontal line, we separate the results achieved by our proposed method from those achieved by other models. As seen from Tables 1 and 2, when using either VUA-20 or VUA-18, MIss WiLDe manages to outperform all other models in terms of F1-score and Recall. It achieves the best Recall on VUA-VERB as well, however performs significantly worse in regards to both the Precision and F1-score. Surprisingly, in other cases an overall weaker variant of our model based on Sentence-BERT yields the best scores out of the 3 variants. The results for VUA-VERB are compared in Table 3. Tables 4 and 5 show the results for Genres and Parts of Speech, respectively. As for Genres, at least one of our sub-models manages to either outperform all other models or at least to draw with one of the competitors. Again MIss WiLDe performs overall better than the other models in terms of Recall, while it loses in Precision. When it comes to Parts of Speech, specifically verbs and adjectives, the base variant of MIss WiLDe proves to be by far the best model in terms of F1-score. This raises the question as to why it is not performing similarly well on the VUA-VERB dataset. The answer might be that for Genres and POS, the results are presented for the models trained beforehand on VUA-18, which provides a significantly larger train set then VUA-VERB. Lastly, Tables 6 and 7 show the results for MOH-X and TroFi. As described in Section 3.2, in the same way as in [PERSON] et al. [10], these datasets are used only for testing with the models trained beforehand on VUA-20. When using MOH-X, the base variant of our model outperforms the competition in terms of F1-score. Neither of our models manages to win on TroFi; it is the model of [PERSON] et al. (2020) achieving the best F1-score and Recall for the said dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Model** & **Precision** & **Recall** & **F1** \\ \hline VUA-ALL-POS & BERT & 75.4\% & 64.7\% & 69.7\% \\ \hline \multirow{6}{*}{VUA-20} & RoB\_BASE & 71.7\% & 60.2\% & 65.5\% \\  & RoB\_SEQ & **76.9\%** & 66.7\% & 71.4\% \\  & DeepMet & 76.7\% & 65.9\% & 70.9\% \\ \cline{1-1}  & MelBERT & 76.4\% & 68.6\% & 72.3\% \\ \cline{1-1} \cline{2-5}  & MsW\_base & 75.4\% & 69.9\% & 72.5\% \\ \cline{1-1}  & MsW\_cos & 75.4\% & **70.3\%** & **72.7\%** \\ \cline{1-1}  & MsW\_sbert & 76.2\% & 66.5\% & 71.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results for VUA-ALL-POS (All Parts of Speech) and VUA-20 (variant of VUA-ALL-POS from [PERSON] et al. ; cf. Section 3.2 for details). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Model** & **Precision** & **Recall** & **F1** \\ \hline VUA-SEQ & MDGI-J-S & 81.3\% & 73.2\% & 77.0\% \\  & MDGI-J & **82.5\%** & 72.5\% & 77.2\% \\  & BERT & 78.0\% & 76.9\% & 77.5\% \\ \hline \multirow{6}{*}{VUA-18} & RNN\_HG & 71.8\% & 76.3\% & 74.0\% \\  & RNN\_MHCA & 73.0\% & 75.7\% & 74.3\% \\  & RoB\_BASE & 79.4\% & 75.0\% & 77.1\% \\  & RoB\_SEQ & 80.4\% & 74.9\% & 77.5\% \\  & DeepMet & 82.0\% & 71.3\% & 76.3\% \\  & MelBERT & 80.1\% & 76.9\% & 78.5\% \\ \cline{1-1} \cline{2-5}  & MsW\_base & 79.6\% & 78.3\% & **78.9\%** \\ \cline{1-1}  & MsW\_cos & 79.3\% & **78.5\%** & 78.9\% \\ \cline{1-1}  & MsW\_sbert & 75.6\% & 71.0\% & 72.7\% \\ \hline \hline \end{tabular}
\end{table}