## 1 Introduction  
The category of concreteness/abstractness (C/A) has been the focus of cognitive research for decades. The problem of representing concrete and abstract objects in the human brain poses a serious challenge to all cognitive science [1]. Concreteness/abstractness is one of the main organizational axes of the mental vocabulary [2]. The main approach to defining these concepts is as follows [3]. Concrete concepts are those that are perceived by the senses. Examples of specific words are cupcake or computer. Abstract concepts are not perceived by the senses--for example,'soul' or 'trust'. Similar interpretations are found in many works. Thus, in the work [4], the following definition is given: 'abstract nouns are those nouns whose denotata are not part of the concrete physical world and cannot be seen or touched'. A similar definition is offered in [5]. To support all such studies, dictionaries with indices characterizing the degree of concreteness/abstractness of words are required. The dictionary is created by polling native speakers, who are asked to rate the concreteness/abstractness of the given words. For the English language, the first major dictionary of this kind was created in 1981 [6]. It containsnearly 4000 words and is freely available from the psycholinguistic MRC database ([[https://websites.psychology.uwa.edu.au/school/MRCDatabase/uwa_mrc.htm](https://websites.psychology.uwa.edu.au/school/MRCDatabase/uwa_mrc.htm)]([https://websites.psychology.uwa.edu.au/school/MRCDatabase/uwa_mrc.htm](https://websites.psychology.uwa.edu.au/school/MRCDatabase/uwa_mrc.htm)) (accessed on 4 May 2022)). Later, a dictionary of almost 40 thousand words was created [5]. Each word receives at least 25 ratings from respondents on a 5-point scale, which are averaged. In addition to English, a comparable dictionary (30,000 words) was created only for Dutch [7]. An apparent problem is the extraordinary complexity of creating such dictionaries. For the German language, the dictionary [8] contains only 4000 words. A database with 6000 words of concreteness/abstractness ratings for the Croatian language has recently been published [9]. The dictionary for the Russian language [10] currently contains 1000 words and is available at [[https://kpfu.ru/tehnologiya-sozdaniya-semanticheskih-elektronnyh.html](https://kpfu.ru/tehnologiya-sozdaniya-semanticheskih-elektronnyh.html)]([https://kpfu.ru/tehnologiya-sozdaniya-semanticheskih-elektronnyh.html](https://kpfu.ru/tehnologiya-sozdaniya-semanticheskih-elektronnyh.html)) (accessed on 4 May 2022). Similar dictionaries have been created for Italian, Chinese, and some other languages. Two obvious tasks arise: (1) increasing the size of the dictionaries and (2) creating dictionaries for other languages, which one can try to solve using modern computer technology, extrapolating from existing human estimates. The main idea in extrapolating estimates to previously unappreciated words is to use vector semantics of words built on a large corpus of texts and obtain new estimates based on the semantic proximity of words in the constructed semantic space. This idea has been implemented in several works. To transfer ratings from one language to another, creating a single multilingual semantic space is appropriate. This approach, to the best of our knowledge, has been implemented in two works [11,12]. In this article, we will apply technologies based on neural networks with deep learning to both of the above problems for the first time. The article is devoted to the following research tasks:  
1. Improve the quality of extrapolation of human ratings for concreteness/abstractness by applying deep learning models. 2. Apply deep learning models to transfer ratings from one language to another and evaluate the quality of ratings obtained in this way. 3. Evaluate the impact of the size of dictionaries when used as a training set and during cross-language transfer. 4.