## 4 Results
### Dependence of the Transfer Quality on the Size of Dictionaries  
We are interested in the question of how the size of the source dictionary and the target dictionary affects the quality of the transfer. We allocate 1000 most frequent words for each language in dictionaries to do this. Since the dictionary for the Russian language contains only nouns, nouns were chosen for other languages as well. In addition, only in two languages, English and Dutch, dictionaries contain a sufficiently large number (over 1000) of adjectives or verbs. Thus, to make all languages equal in terms of the size of the word sets used for training and testing, we report results only for nouns. Moreover, our preliminary experiments with adjectives show lower results (correlation is less than 0.6). Table 4 presents correlation coefficients in 1000-word dictionary to 1000-word transfer. Comparing Tables 3 and 4 shows that all coefficients except one become higher when a complete dictionary is replaced with a 1000-word dictionary. At first glance, this result \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{EN} & \multicolumn{3}{c}{DE} & \multicolumn{3}{c}{NL} & \multicolumn{3}{c}{RU} & \multicolumn{3}{c}{HR} \\ \hline
**Source Lang.** & **M1** & **M2** & **M3** & **M1** & **M2** & **M3** & **M1** & **M2** & **M3** & **M1** & **M2** & **M3** & **M1** & **M2** & **M3** \\ \hline EN & - & - & - & 0.68 & 0.71 & **0.73** & 0.57 & 0.60 & **0.65** & 0.73 & 0.77 & **0.8** & 0.51 & 0.54 & **0.59** \\ DE & 0.70 & **0.73** & 0.71 & - & - & - & 0.60 & **0.63** & **0.63** & 0.71 & 0.77 & **0.79** & 0.53 & 0.55 & **0.60** \\ NL & 0.72 & **0.76** & 0.74 & 0.69 & **0.73** & 0.70 & - & - & - & 0.68 & **0.73** & **0.73** & 0.46 & 0.50 & **0.58** \\ RU & 0.62 & **0.72** & 0.70 & 0.58 & **0.68** & **0.68** & 0.45 & 0.56 & **0.58** & - & - & - & 0.48 & 0.53 & **0.56** \\ HR & 0.65 & 0.66 & **0.68** & 0.64 & 0.67 & **0.70** & 0.52 & 0.50 & **0.57** & 0.69 & 0.73 & **0.78** & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: [PERSON]’s correlation coefficient for complete dictionaries. Notation: ‘M1’ stands for the distilled multilingual BERT, ‘M2’ stands for the multilingual BERT, ’M3’ stands for the MS-MiniLM (bold face corresponds to the best model for a language). Figure 2: Histogram of concreteness ratings from Dutch dictionary. seems unnatural, but in the work [43], a similar result was obtained using the material of emotive vocabulary. On a 1000-word dictionary, machine extrapolation of the data gives a better correlation coefficient than on a larger one (a dictionary with 14 thousand words); this is the case for both English and Spanish. A similar result was obtained in [44], also for an emotive vocabulary. However, a direct comparison with the results of these two articles is impossible since both the size of the training set and the test set differ proportionally. In this paper, the difference is not proportional. Therefore, it makes sense to take a closer look at the influence of the size of the source and target dictionaries in our task; we separate these two factors. We compare four transfer options: from a dictionary of 1000 words to a dictionary of 1000 words (1000 -> 1000), from a complete dictionary to a dictionary of 1000 words (all -> 1000), from a dictionary of 1000 words to a complete dictionary (1000 -> all ), and from the full dictionary to the full one (all -> all). As mentioned above, the best results are achieved by MS-MiniLM. The results are presented in Figures 3 and 4. The y-axis shows the Spearman correlation coefficient. Numerical data are given in the Appendix A (Tables A1-A5). We consider two separate cases: (A) the size of the source vocabulary is fixed while the target vocabulary changes, and (B) the size of the target vocabulary is fixed while the source is changed. 1. The size of the source vocabulary is fixed while the target vocabulary changes. This is shown in Figures 3 and 4 by moving from the first block of columns to the third and from the second to the fourth.