## 4 Results
### Cross-Lingual Transfer of Concreteness Estimates  
The presence of good large dictionaries for some languages raises the question of transferring ratings from one language to another. This is possible thanks to the existence of multilingual neural networks pre-trained in several languages simultaneously.  
In the first series of calculations, the dictionaries were used entirely. In all models, the hyperparameters bs = 64, 3 epochs were used in all models. The best result was shown by the MS-MiniLM model. Table 3 shows [PERSON]'s correlation coefficients between ratings transferred from the source language with their values in the target language for three models. [PERSON]'s coefficients are, on average, 0.01 less. The best-achieved result (for Russian) exceeds the result of work [11].  
For the multilingual BERT model, the results are, on average, almost the same, only slightly lower, but MS-MiniLM provides the absolute best coefficients among all transfers. These are the coefficients for transferring from English and from German to Russian, 0.8 and 0.79, respectively. The results shown by the distilled multilingual BERT model are noticeably worse.  
When considering Table 3, it is noteworthy that for different languages, the results differ quite significantly. The hardest-to-obtain rankings are for Dutch and Croatian. In Figure 2 is a histogram of word scores for Dutch.  
\begin{table}
\begin{tabular}{c c c} \hline
**Train Size** & **[PERSON] (\(R_{S}\))** & **Pearson (\(\rho\))** \\ \hline
1000 & 0.860 & 0.870 \\
4000 & 0.890 & 0.900 \\
35,000 & 0.910 & 0.920 \\ \hline \end{tabular}
\end{table}
Table 1: Dependence of the quality of approximation on the size of the training set.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Word** & **Human Rating** & **Machine Rating** & **Absolute Diff.** \\ \hline day & 0.730 & 0.730 & 0.000 \\ communication & 0.475 & 0.475 & 0.000 \\ connection & 0.500 & 0.501 & 0.001 \\ wall & 0.965 & 0.966 & 0.001 \\ degree & 0.500 & 0.501 & 0.001 \\ lingerie & 0.968 & 0.508 & 0.459 \\ fax & 0.870 & 0.388 & 0.482 \\ general & 0.155 & 0.708 & 0.553 \\ past & 0.175 & 0.754 & 0.579 \\ guy & 0.920 & 0.261 & 0.659 \\ \hline \end{tabular}
\end{table}
Table 2: Discrepancy between machine and human estimates (both minimum and maximum differences with respect to MAE).  
The histogram shape is noticeably different from similar histograms for English and Russian languages. Noteworthy is the non-smooth character of the histogram, with statistically unexplained peaks, which may be a consequence of incorrect post-processing of empirical data or may be due to numerical binning issues or anchoring to discrete levels.  
In Table 3, one can see a significant difference between the coefficients when transferred into Russian and from Russian (the average difference is 0.145). One of the possible explanations is that the Russian dictionary contains only 1000 of the most frequent words (nouns) of the language, for which there were a lot of data in the corpora on which the neural networks were pre-trained, and they are better represented in models; for them, it is easier to predict the extrapolated ratings. For other languages, especially English, the dictionaries are much larger and contain more rare words, the ratings for which are more difficult to predict. We then conducted a study of the dependence of the results on the volume of dictionaries.