## 5 Discussion and Conclusions  
Various models and methods have been used to extrapolate human concreteness/abstractness ratings to obtain large vocabularies: LSA, GloVe, fastText, etc. In this work, the most modern models were used for the first time: BERT, MS-MiniLM. With extrapolation within one language, it was possible to improve all previous assessments of the quality of the dictionary, to bring them to a value of 0.910 according to Spearman (for the English language), with an expected marginal estimate of 0.919. The BERT model can be recommended for the extrapolation of other ratings (valence, etc.) and in other languages. A prerequisite is the pre-training of BERT in a given language or the presence of a large body of texts required for training the model. A human-rated dictionary containing around 1000 words is also in demand. In the absence of a vocabulary with human ratings for a language, an approach related to transferring ratings from one language, where they already exist, to another can be applied. Previously, this approach was used in combination with the automatic translation of words, thus creating a single semantic space for several languages. The best previously achieved result on this path is 0.76 [11]. The best score that we obtained is 0.843, which is close to the scores in the single-lingual case. Comparison of the results of neural networks of different architectures showed a noticeable advantage of MS-MiniLM. In addition to the architecture of neural networks, the result is influenced by many other factors analyzed in this article. First of all, these are the sizes of the dictionaries of the source and target languages. In this paper, it is shown that an increase in the training set is important for the one-language case but not essential for the cross-language \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Language Combinations** & **Multilingual Transfer** & **Monolingual Transfer (from 4 Languages)** & **Mean** \\ \hline DE + RU + NL + HR \textgreater{} EN & 0.832 & 0.83 (DE), 0.79 (RU), 0.82 (NL), 0.83 (HR) & 0.818 \\ \hline EN + RU + NL + HR \textgreater{} DE & 0.790 & 0.77 (EN), 0.73 (RU), 0.77 (NL), 0.75 (HR) & 0.755 \\ \hline EN + DE + RU + HR \textgreater{} NL & 0.725 & 0.69 (EN), 0.72 (DE), 0.61 (RU), 0.66 (HR) & 0.675 \\ \hline EN + DE + NL + HR \textgreater{} RU & 0.778 & 0.80 (EN), 0.81 (DE), 0.74 (NL), 0.79 (HR) & 0.785 \\ \hline EN + DE + RU + NL \textgreater{} HR & 0.674 & 0.68 (EN), 0.68 (DE), 0.67 (RU), 0.67 (NL) & 0.675 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: [PERSON]’s correlation coefficient for the MS-MiniLM model (1000-word dictionaries). transfer. A 1000-word vocabulary is sufficient for fine-tuning the models and obtaining a high-quality vocabulary in the target language. The article is the first to consider the option of transferring from mixed data of several languages to the target language. It turned out that this led to some improvement in the results. It is hypothesized that linguistic diversity in source data may be a favorable factor. When considering different languages, the hypothesis about the importance of the typological proximity of languages is suggestive. However, it is not confirmed by our data. Russian and Croatian are Slavic languages; the other three are Germanic. The linguistic affinity of the languages did not provide the best result when transferring from Russian to Croatian and vice versa compared to the Germanic languages. Thus, the main results are as follows:  
1. We presented a deep learning model and applied it in the task of extrapolating human concreteness/abstractness ratings to achieve a result that exceeds those previously published. 2. The methodology for transferring ratings from one language to another is described; estimates of the quality of the ratings are obtained. 3. It has been established that dictionaries of 1000 words in size provide a sufficiently high quality of extrapolation when used as a training set; in a cross-language transfer, they give almost the same quality as large dictionaries. 4.