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-The final stage includes the arrangement of tagged texts in the form of an information retrieval structure, called a corpus manager. This system makes it possible to quickly search for texts by a variety of criteria and provides for their statistical processing.

-Providing access to the corpus. Free access to large-size text arrays is possible both in the display class and through distribution on digital media, CD-ROMs, as well as in the global network mode. Different categories of users may be granted different rights and capabilities.

Turning to the issue of mechanisms of text corpora functioning and the tools of corpus linguistics, it should be noted that the search in the corpus is carried out at the user’s request, using special programs, named corpus managers. With their help, the search results are sorted, the statistical calculation of frequently used variants is performed, and concordances are compiled. Most modern corpus managers allow one to perform all types of search for various textual units, namely, the search for specific word forms; the lemma-based search for word forms (i.e. the search for all forms of the same lexeme found in the text); the search for inseparable and discontinuous phrases.

With appropriate tagging, the main criteria of search is a set of necessary morphological characteristics (for instance, the search for all Russian word combinations, having the model of

«a preposition к + a noun in the dative case», English set phrasal combinations with the verb take, etc.) and other information, correlating with the levels of linguistic tagging, presented in the corpus. The availability of meta-tagging enables a user to form his own sub-corpus of texts, selected on the basis of various characteristics. Each of the examples is supplied with information about the source of borrowing. In some corpora, it is also possible to obtain complete statistical information about the requested language expression, namely, its percentage in the general array of the corpus given, the distribution by genre or time reference, and the information about the frequency of its combinability.

Let us turn to the description of the tools of corpus linguistics, realizing its practical poten-

tial.

One of the important corpus elements of the analysis is generation of concordance lines. Concordance is defined as the joint use of words in a fixed-length context, and the concordance function allows us to analyze the joint use of the desired word forms. In comparison with a text editor, the possibilities of concordance are more extensive. With its help, it is possible to analyze not a single text, but a group of texts; identify the frequency of using language units and get an idea of the nature of their combinability; request the desired units, using more complex forms, compared with those, offered by the text editor; provide the ability to refer to a specific text in which an example was found; offer various options for presentation of information (i.e. in the form of complete sentences or with omission of the desired lexical unit) [9, pp. 85-89].

The linguistic corpus is also used to search for collocations, which are defined as statistically stable phrases [10, p.57]. The tool of collocation search enables us to record the nature of word usage in context (for example, as part of a terminological group), determine the left and/or right environment of a lexical unit and, thus, obtain information about the lexical and grammatical combinability of a word. At the same time, such statistically stable combinations can be both phraseologized and free. The main feature of collocation is the fact that one of its components retains its lexical meaning, while the phrase as a whole maintains its stability. Besides, collocations are characterized by absence or weakening of the figurative and emotionally evaluative meaning.

An important tool for corpus analysis is the function of keyword search in context (in English, the keyword in context, KWIC). This function gives specialists the opportunity to get information about the contexts of the desired word usage, trace and evaluate the changes in its semantics depending on different contexts, its grammatical and lexical combinability.

Finally, the «extended context» function allows one to view several sentences of the source text and, thus, trace the evolution of a communicative situation in which a certain word

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was used, as well as to get some information about the time and circumstances in which the text under analysis was created.

Before turning to the issue of the nature and types of using corpus methodology in profes- sionally-oriented translation training, let us note that a corpus of parallel texts possesses a number of specific didactic properties, which determine the methodology for developing the trainees’ professionally-oriented translation competence. According to many scholars, such properties include the multilingual structure, the contextuality of search results and the possibility of restricting the area of language usage. The practical implementation of each of these properties is associated with the achievement of the following methodological goals:

-The multilingual parallel text corpora are used to form and develop the trainees’ skills of professionally-oriented translation (and, with its help, the skills of foreign-language profession- ally-oriented speech);

-The contextuality of search results contributes to development and mastering of trainees’ skills to precisely translate the meanings of new words on the basis of the language context, and also helps students to familiarize with the peculiarities of word usage and translation in real language situations, as well as with the nature of linguistic combinability of various units;

-The ability to establish the parameters of the area of language usage when implementing a search query involves the development of the trainees’ ability to more accurately define the meanings of new words and translate them in the requested specific area of communication.

On the basis of the methodology of using parallel text corpora, it is possible to single out the following forms of teaching translation (the systems of contextual translation search, Linguee and Reverso, can be used as an e-resource):

-the task of translating professional terms in the corpus of parallel texts. This form of classroom work involves the selection of the correct semantic equivalent or an occasional translation variant for an original term, determined by the text content and its affiliation to a particular domain of knowledge. For example:

1.It shall be possible to f a s t e n the device in no more than one action. - Должна обес-

печиваться возможность з а т я г и в а н и я приспособления одним простым движением.

2.Regulations will be formulated on benefit sharing between the creators and the users of scientific and technological achievements (scientists, entrepreneurs and service providers) so as to f a s t e n the link between scientists' responsibility and the products of their scientific and technological research - Будут подготовлены правила, регулиру-

ющие совместное использование выгод теми, кто обеспечил научно-технические достижения, и их пользователями (ученых, предпринимателей и поставщиков услуг), с тем чтобы з а к р е п и т ь взаимосвязь между деятельностью ученых и результатами их научно-технических исследований.

3.Please return to your seats and fasten your seatbelts - Пожалуйста, вернитесь на свои места и п р и с т е г н и т е ремни безопасности.

This example shows that the presence of «attachment» and «strength» semes in the meaning structure of the term fasten allows the use of various occasional variants in translation, which are determined by contextual environment of the term indicated (for example, in the first and the third cases, the context points to the mechanism of action of an object, serving as a fixing tool, while in the second case, it points to enhanced properties of the process under way, leading to a positive result). The knowledge of the semantic structure of a term, obtained on the basis of studying the comparative data of the text corpus, as well as the idea of the subjectmatter of the translated text allows the trainees to choose the specific translation variant of the term, fixing it in memory for subsequent use in similar translation situations.

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- the tasks of identifying the translation differences between terms, similar in meaning (for example, strength and force, profit and benefit, removal and withdrawal, etc.). These tasks are performed using the methods of contextual analysis and concordance search. For example:

1.Some highlighted the r e m o v a l of trade distorting agricultural subsidies as a priority. - Некоторые обозначили у с т р а н е н и е приводящих к отклонениям в торговле сельскохозяйственных субсидий в качестве одного из основных приоритетных направлений деятельности.

2.Member States had been withdrawing or considering their w i t h d r a w a l. - Госу-

дарства-члены выходили или рассматривали вопрос о в ы х о д е и з с о с т а в а Организации.

In the process of performing this task, the trainees determine the range of combinability of similar terms and, on the basis of contextual analysis, reveal a semantic difference between them (in the example given, the semantic structure of removal conveys the idea of eliminating the problem or removing a negative factor, while withdrawal implies the idea of termination of participation in the work of a particular organization or in a particular activity).

- the tasks of identifying the differences between occasional translation variants of same professional terms (for example, the equivalents of company are company, firm, partnership, society, and a number of other options). The performance of this type of tasks is also associated with the use of the concordance method based on students’ background knowledge of relevant professional domains.

Each of these types of tasks fully implements the possibilities offered by the corpus technique in the field of contextual analysis of language units.

Conclusion

The methods of parallel text corpora are a highly effective innovative addition to traditional scientific and educational technologies in the field of language theory and translation studies. These methods combine such aspects as authenticity, representativeness and the ability of students’ independent work. The creation of a text corpus is considered as a sequence of stages, including both the proper language operations and the stages, associated with technical support of the textual material analysis. The value of a text corpus is determined not only by various language content of the presented textual information, but also by the convenience of its structural and visual presentation (for example, the Linguee corpus has an in-built feature for graphically highlighting the parallel text fragments under analysis). The tooling of text corpora and contextual search systems allows us not only to record the features of the use of certain linguistic units in the text, but also to obtain comprehensive information about the content and formal features of the texts themselves, their genre affiliation and stylistic relatedness. In the applied aspect, text corpora are the most important means of teaching professional-oriented translation, providing the possibility of developing a number of tasks aimed at developing the skills of lexical analysis of the perceived information and its translation into a foreign language.

References

[1]Avdeev A.A., Kovyrshina E.O. Ispol'zovanie parallel'nyh korpusov tekstov v mashinnom perevode [Tekst] / A.A.Avdeev, E.O.Kovyrshina // Antropocentricheskie nauki: innovacionnyj vzglyad na obrazovanie i razvitie lichnosti. Materialy VII Mezhdunarodnoj nauchnoprakticheskoj konferencii (Voronezh, Rossiya, 21-22 marta 2018). – Voronezh: 2018. – C. 223225.

[2]Belyaeva L. N. Korpusnaya lingvistika i perevod: potencial i ogranicheniya // Trudy mezhdunarodnoj nauchnoj konferencii «Korpusnaya lingvistika – 2011». – 2011. – S. 86-91.

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[3]Shevchuk V. N. Informacionnye tekhnologii v perevode. EHlektronnye resursy perevodchika – 2. M.: Zebra E, 2013. 384 s.

[4]Rajskina V.A., Dubnyakova O.A. Primenenie sovremennyh metodov korpusnoj lingvistiki pri analize teksta (na primere korpusa BFM) Sbornik statej po materialam XXV studencheskoj mezhdunarodnoj zaochnoj nauchno-prakticheskoj konferencii. — M.: MCNO, 2015. - 214 s.

[5]Zaharov V.P. Korpusnaya lingvistika: Uchebno-metodicheskoe posobie / V. P. Zaharov. – SPb., 2005. – 48 s.

[6]Avdeev A.A. Razvitie perevodcheskoj kompetencii studentov na osnove korpusnoj metodiki [Tekst] / A.A.Avdeev // Antropocentricheskie nauki: innovacionnyj vzglyad na obrazovanie i razvitie lichnosti. Materialy II Mezhdunarodnoj nauchno-prakticheskoj konferencii (Voronezh, Rossiya, 27-28 fevralya). – Voronezh: 2015. – C. 111-112.

[7]Amieva A.M., Filimonov V.V.,Sergeev A.P. Osnovnye metodiki issledovaniya struktury teksta // Peredacha, obrabotka, vospriyatie tekstovoj i graficheskoj informacii. – Ekaterinburg, 2015. S.251-263.

[8]Boguslavskij I. M. i dr. Annotirovannyj korpus russkih tekstov: koncepciya, instrumenty razmetki, tipy informacii” // Trudy Mezhdunarodnogo seminara po komp'yuternoj lingvistike i eyo prilozheniyam \"Dialog-2000\". Protvino, 2000.

[9]Dominikan A.M. Konkordans kak instrument dlya izucheniya znacheniya slova //

Vestnik TvGU. Seriya «Filologiya», 2016. № 4. – S. 58-59.

[10]Pavel'eva T.YU. Izuchenie kollokacij na osnove lingvisticheskih korpusov tekstov //

Vestnik Tambovskogo universiteta. Seriya «Gumanitarnye nauki», T. 3-4, 2016.

[11]Kokoreva A. A.Korpus parallel'nyh tekstov v obuchenii inostrannomu yazyku // Vestn. Tambovskogo universiteta. Seriya: Gumanitarnye nauki. – 2013. – № 2. – S. 57-62.

[12]Kutuzov A. B. Korpusnye metodiki v perevodovenii i prepodavanii perevoda // Materialy mezhdunarod. nauch.-metod. kollokviuma «Problemy bilingvizma v sovremennom mezhkul'turnom diskurse» – Perm', izd-vo PGTU, 2011. – S. 368-373.

[13]Tarnaeva L.P., Osipova E.S. Ispol'zovanie resursov korpusnoj lingvistiki pri podgotovke perevodchikov v sfere professional'noj kommunikacii // Filologicheskie nauki. Voprosy teorii i praktiki. Tambov: Gramota, 2016. № 9. CH. 1. S. 205-209.

[14]Vvedenie v korpusnuyu lingvistiku [Elektronnyj resurs] // Rezhim dostupa: http: //www.myshared.ru/slide/472948/ (data obrashcheniya – 25.11.2015).

[15]Elektronnaya baza dannyh kontekstual'nogo poiska Linguee [Elektronnyj resurs] // Rezhim dostupa: http: //www.linguee.ru/ (data obrashcheniya – 25.04.2019).

[16]Elektronnaya baza dannyh kontekstual'nogo poiska Reverso [Elektronnyj resurs] // Rezhim dostupa: http: //www.context.reverso.net/ (data obrashcheniya – 25.04.2019).

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UDC 81'25

THE USE OF CLOUD TECHNOLOGIES IN TEACHING

COMPUTERIZED TRANSLATION

E.V. Kashkina, T.V. Gilyarovskaya

____________________________________________________________________________

Voronezh State University Candidate of historical sciences,

associate professor, Department of French Philology Elena Viktorovna Kashkina

e-mail: uelha@bk.ru

Voronezh State University Lecturer, Department of French Philology Tatyana Viktorovna Gilyarovskaya e-mail: tatyana_gilyar@mail.ru

____________________________________________________________________________________________

Statement of the problem. This article discusses the features of teaching students how to work with Translation Memory (TM) programs. These programs are an auxiliary tool of a translator when working with scientific and technical texts. In this capacity, acquaintance with the work of such programs can also be useful to students of non-special universities dealing with industry vocabulary and translation of highly specialized texts.

Results. The practical experience of classes with translation memory programs using cloud technologies is presented. This synthesis (TM programs and cloud technologies) provides an opportunity to study not only in stationary equipped computer classes, but also in any audience with Internet coverage. Translation memory programs in this algorithm can also be used for any audience of students. In the process of analyzing the algorithm for using translation memory programs, the sequence of application of methods for working with cloud data was developed, the analysis of the methodology of this type of work was carried out, and the specifics of translation training using cloud technologies and Translation Memory programs were determined.

Conclusion. Learning the operation of a translation memory program is the sharing by students of TM programs, in particular, such program modules as translation memory, terminology bases, creating and editing translation projects. Practical classes (for undergraduate students of the 3rd course and for the 4th course specialist, direction of translation and translation) showed that TM programs are an effective tool for translating technical texts by linguistic students. Efficiency is achieved by using common vocabulary and terminology bases, on special topics, which speeds up the process of translating large texts of the same type.

Key words: computerized translation, translation memory programs, cloud technologies, translation of highly specialized texts.

For citation: Kashkina E.V., Gilyarovskaya T.V. The use of cloud technologies in teaching computerized translation / E.V. Kashkina, T.V. Gilyarovskaya // Scientific Journal “Modern Linguistic and Methodical-and-didactic

Researches”. – 2019. - № 3 (26). – P. 118-125.

Introduction

Information technologies are increasingly used in modern education, including the teaching of a foreign language. However, the subject of teaching students to translate "using Internet services remains insufficiently illuminated and methodologically developed" [1, p. 116-119].

The goal of this article is to present methods of teaching computerized translation using cloud technologies in practical French classes and show the work algorithm and examples that can be useful in working with translators.

Purpose this article is the definition of the patterns of formation in students of professional skills and skills of working with translation memory programs using cloud technologies.

___________________________________

© Kashkina E.V., Gilyarovskaya T.V., 2019

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To achieve this goal, a methodology for teaching students to use computerized translation tools is presented.

Research Methodology

The study is based on more than five years of experience in teaching computerized translation to students, future translators, and many years of practical work as a translator in international projects, including programs with translators.

Authentic texts of contracts, tender documentation in French on general construction and hydro construction topics, with professional Russian translation, with which we had to work on construction sites of Russian companies in North African countries, were used as material for the study.

In the course of the study, the following methods were used: observation method, analysis, statistical method, generalization method.

The results of the study

The experience of practical work in the framework of international projects has shown, firstly, the efficiency of the collective work of translators on large volumes of technical texts using the “translation memory” programs, which significantly speeds up the translation process.

Secondly, the experience of teaching the “Computerized Translation” discipline to linguistic students presented the possibilities and ways to bridge the gap between the linguistic education of students and the tasks of translating highly specialized technical texts, since it is these specialists who are increasingly in demand in the market of translation services. All this made it possible to formulate some recommendations on the formation of a specific algorithm and methods of teaching how to work with “translation memory” programs, as well as mastering with them the skills of working with texts in a specific area (hydro, oil and gas, geological, etc.). When working with “translation memory” programs, students demonstrated skills in search and analytical work and cognitive activity, the ability to solve non-standard tasks for which knowledge of the original language and translation is not enough. The material acquired during the classes: glossaries, terminology bases, etc. could be the basis for the future work of translators.

Currently, there are two fundamentally different approaches to the use of computer translation systems. The first is a group of programs called MT-programs (from Machine translation - machine translation), automated translation, Prompt and on-line resources like Google, Yandex, etc. It is widely used and popular not only among professionals, as it helps to get acquainted with the general content of texts, websites and other information.

Programs of the second group are called TM-programs (from the Translation memory - translation memory), which are installed on the device and can work together with the user. The basis of the TM programs are specialized databases that contain previously translated text units. If a unit that is already in the database is found in the new text, the system automatically adds it to the translation. Thus, “the program will instantly remind you how this phrase was translated last time. Access to information is easy and fast” [2]. It is most effective to use such programs in the process of translating technical texts, since their style, as a rule, is formal, limited in scope and replete with repetitions of parts of content and generally accepted introductory turns.

Computerized translation classes, as a rule, are held in computer labs with traditional software installed on computers (Microsoft Windows, MS Office, translators, electronic dictionaries), which requires significant investments. In addition, the need for ongoing service support and software updates. The cost of TM programs is quite high and, therefore, the number of copies of the programs used for training is limited. The time frame of the practical course is also small and therefore requires maximum concentration. Thus, teachers of a computerized translation course sometimes have to mainly concentrate on theoretical lessons and slideshows. The computer class is also not always available for use. Some TM developers (for example,

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Déjà vu X3 Free and others) [3] offer trial free 30-45-day versions. Their functionality is somewhat truncated with respect to the full versions. Trial versions allow you to gain some skills in computer-aided translation (“automated translation”), but they are not sufficient for confident use.

An alternative way out, as practice has shown, can be cloud technologies and the Internet. Cloud technologies allow using programs without installing them on a computer, saving on the acquisition, support, upgrades of software and hardware. Currently there are many cloud solution providers. Large companies: Amazon, Google, Microsoft and others offer discounts to educational institutions for access to cloud services with significant savings [4].

“Cloud technologies” or “cloud computing” (cloud computing, fr. Technologies en nuage) is a simple and practical technology that allows you to communicate in social networks, use online banking services, listen to music from Internet sources, corresponded by e-mail,

“even such a simple action as a request for a page of a site is an example of cloud computing”

[5]. The terms cloud and cloud computing were first heard in one of the speeches of the head of Google back in 2006 [6] and today this is one of the most popular features.

Cloud TM-systems have a number of advantages, compared with the same products for the PC. Among them - the possibility of simultaneous work on one document of several translators and a significantly lower amount of initial investment due to the use of a business model with a monthly fee. Such translation systems work on any device that supports a web browser, so students can use their own laptops, tablets and smartphones. In this case, there is even no need for a computer lab. Despite the fact that the market for translation programs has been developing dynamically and many TM programs have existed for a long time, their use is not yet widespread due to the opinion about the complexity of their installation, training and high cost. At present, "many of the programs have been greatly improved, and are of particular interest for translators of all languages, in particular, Eastern languages." The latest versions are created

“taking into account the requirements that are often put forward by translators to such programs, namely accessibility, ease of use and efficiency in translation activities” [7, p. 201–203]. The main condition for working in the cloud is having access to the Internet.

TM programs are designed to work with texts, where there are repeated words, expressions and whole fragments. This format is inherent in texts related to industry topics. Translation of highly specialized texts by linguistic students causes great problems. Difficulties in translating and teaching professional-oriented translation are associated exclusively with the philological education of translators for whom “industry texts are a priori lacunar, that is, meaningfully opaque” [8]. The purpose of the course is the formation and development of students' skills to translate in a specific area. The specificity of highly specialized texts is that their

“main function is to inform, communicate certain information,” therefore, “the main task of the translator of such texts is the most complete transfer of information contained in them, ensuring invariance at the content level” [9]. Therefore, when working on specialized texts, translators are faced with the fact that a large amount of time is spent searching and comparing the necessary terms in dictionaries, packages, and bases. Translation memory programs store previously translated text fragments, words and phrases that can be prompted by the translation engine.

Due to the fact that the drive of transfers is an integral part of TM programs, students were offered the following sequence of learning computer translation tools.

At the initial stage of the learning process of TM programs, students were introduced to the work of the translation drive on the Internet. As an example, the search for the translation of the word oued was proposed, which appears in the material intended for learning how to work with translation memory programs. For translation, the students were given the text of the document “Contrat de réhabilitation du cité de Maître d'ouvrage à la design du Mrage M'Dez (oued Sébou, Sefrou province)” - (“The contract for the repair of the Customer’s construction camp on the construction of the M’Dez dam, on the river Cebu in the province of Sefra”), 11 pages in length, which was a working document for construction in Morocco. Problems with the verifi-

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cation of the terms did not arise, since the text in French has a professional translation into Russian. Some construction terms were given to students and loaded by the teacher into the database.

The translation drive showed about 70 sentences with the phrase oued, found in 9 months, some of which were repeated [10]. Below are some translation examples taken from the multilingual corpus of translated United Nations documents [11].

Oued: dans les régions arides, cours d'eau généralement asséché où l'eau ne coule qu'après de fortes pluies [11].- Уэд - in dry regions, usually a dry river where water does not flow until it rains heavily [11].

La qualité des eaux usées déversées dans les oueds, les cours d'eau et la Méditerranée à Gaza doit être conforme aux normes internationales les plus récentes [11]. - Wastewater discharged into reservoirs, streams and the Mediterranean Sea in the Gaza Strip must meet modern international standards [11].

La Commission a reconnu que les fermes de Chebaa faisaient partie intégrante du Liban, bordées à l’est par le Oued connu sous le nom de Wadi al-Asal [11]. The commission concluded that the Shab’aah sites should be considered a region of Lebanon, adjacent in the east to a dried-up channel known as Wadi al-Asal [11].

Ces facteurs ont une forte incidence sur l'efficacité de l'utilisation des précipitations recueillies, sur le ruissellement des eaux de surface dans les cours d'eau et les oueds, ainsi que sur la recharge des aquifères souterrains[11]. - These factors strongly affect the possibility of efficient use of water that fell in the form of rain, and significantly affect the volume of surface runoff in streams and dry river beds, wadis, and the replenishment of aquifers in groundwater [11].

Durant la saison des pluies, la police ne pouvait pas avoir accès, selon elle, à certains camps et villages à cause des cours d’eau temporaires (oueds) [11]. - During the rainy season, the police claimed that it could not reach some camps and villages due to the seasonal flood of the rivers (wadi) [11].

Dans le cadre du projet de développement de la vallée Aïn Draham-Tabarka, l'APEL a mis en place un centre de formation de jeunes filles à Oued Ezzène [11]. - Within the framework of the Ain-Draham-Tabarka Valley development project, the Association has established a vocational training center for girls in Oued-Ezenet [11].

The examples above demonstrate translations with a context, which is especially important for novice translators. Working with the drive develops students the skill of searching and choosing the necessary term. As a rule, then translators compile their own glossaries, where all verified terms are listed, and create memory bases.

Sharing memory bases of linguistic students is carried out by providing access to the project to other users. The whole student group can start working on the document at the same time. Students see each other's translations at the moment when the sentence is entered into the database of translation memory, which makes it possible to achieve unity of terminology and teach collective work. Also, cloud TM-systems offer additional opportunities for their use in the following areas:

-machine translation (for optimization of work and post-editing);

-instant messaging (to discuss issues with the group in real time);

-quality control (with automatic verification of the unity of terminology and consistency of the translation base, as well as the preservation of tags, formatting and numerals).

Based on these capabilities, we can formulate the following requirements for TM programs that are necessary for working in practical classes: a text that is most representative of such work, i.e. ensuring the relevance of the material being translated, to include the correct term in the system; translation memories and terminology databases must be available online.

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Thus, to work in practical classes, high-speed Internet is used to access cloud-based TM programs. Depending on the tasks, cloud systems are divided into private, public, hybrid and public. In other words, information may be available: to a limited number of individuals (translators) who were granted access by the administrator (owner) of the cloud platform; a group of translators working on the same project; everyone. To work with TM in the classroom, the cloud platform is created by the teacher based on the server version of the translation memory program, for example, DéjaVu X3. The joint work of students in the process of learning the translation technology using TM programs in the cloud can have the following sequence:

1.Students register and get access to a cloud translation system.

2.The teacher creates a document for translation, divided into segments, and provides students with access to it (through a link sent to them by e-mail, VK social network, etc.).

After these steps, students connect to their personal desktop. They start working with the system in the classroom under the guidance of a teacher, filling the memory of the translation. At the same time, students exchange translation bases and terms, a glossary, and can advise on choosing the most appropriate translation option with the teacher, for unifying the terminological base and preserving the unity of the document style. If necessary, students' test work can continue at home, and the availability of Wi-Fi will allow working on the document on the road. Obviously, the translation memory programs of the TM are a system that itself “is constantly in the learning mode” [12].

Initially, the TM program is a clean sheet with memory that will “remember” everything that a translator will do. The algorithm for using archive data is quite simple: the work takes place with three active windows. One window is the source text, the second is the active translation, the third is the prompt of the finished translation that was previously encountered and uploaded to the data archive. If the expression in the translated text has already been encountered in a previous work and has been loaded into Translation Memory, it will necessarily appear in the window with hints. Such hints are especially valuable when translating technical or computer documentation, in which many expressions are repeated very often. In the proposed text for the translation of the contract “Repair of the construction campus”, for example, sentences were chosen with the phrases la réception définitive - final acceptance and la réception provisoire - temporary acceptance.

Le contrat comprend l'exécution et l'achèvement des travaux dans les conditions spécifiées dans le cahier des prescriptions spéciales (CPS), ainsi que l'entretien des ouvrages jusqu'à la date de la réception définitive [1* p. 5]. - The contract includes the conduct and completion of works according to the Technical Specifications (CPS), as well as maintenance of facilities before final acceptance [1 * p.5].

Further, the program itself prompts previously translated fragments.

Les plans de récolement et les notices nécessaires de tous les lots doivent être remis à la société Z, au plus tard, quinze (15) jours après la réception provisoire, et avant l’établissement du décompte dernier [1* p. 6].

Transfer: Operational drawings and necessary notices for all sites must be submitted to Company Z no later than fifteen (15) days after the temporary acceptance, prior to the last decontation [1 * p. 6].

Difficulties caused translation of the last word décompte, due to the fact that in Russian the word calculation has several meanings. The decont (tracing) option was used as one of the specific nominations. This phenomenon is observed when companies use terminology that is not fixed by bilingual dictionaries.

La réception définitive est prononcée un (1) an après la date du procès-verbal de la réception provisoire [1* p. 6]. Translation: Final acceptance is announced one (1) year

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after the date of signing the preliminary acceptance protocol [1 * p. 6].

Word combination la date du procès-verbal de la réception provisoire - date of signing the preliminary acceptance report repeated in other parts of the document. In the future, the text of the contract includes 10 phrases with the phrase réception définitive and 12 with the phrase réception provisoire. And the program substitutes not only a phrase, but larger fragments.

One of the basic functions of TM programs is “search in concordance” or simply “concordance” applied to CAT. It is often required to see how a whole segment was previously translated, but a separate word, for which a concordance search is used, which will find the segments with the necessary word in the translation memory. For example, the following clause was proposed with the word le relevé, having multiple meanings: statement; list; statement; measurement; shooting, etc.

As an assignment, students were indicated sentences containing this word in the program’s database, so that they could translate it in the future in a uniform manner. Students perform a database search, during which CAT provides a list of segments, where the word le relevé in the original sentence is highlighted. Next, students need to select the appropriate translation option from among those offered by the program.

The teacher specifies sentences with this word in the vocabulary database of the program, so that students translate it in the future. Next, students should look at the translation options and select the appropriate ones in this case.

Lorsque les ouvrages doivent être ultérieurement cachés ou inaccessibles et que, par suite, les quantités exécutées ne seront plus susceptibles de vérifications, la société B doit en assurer le relevé contradictoirement avec la société Z Si la société Z estime qu'une rectification doit être apportée au relevé proposé par la société B, le relevé rectifié doit être soumis à la société B pour acceptation. Si la société B refuse de signer ce relevé ou ne le signe qu'avec réserves, il est dressé un procès-verbal de la présentation et des circonstances qui l'ont accompagnée. La société B dispose alors d'un délai de quinze (15) jours à compter de cette présentation pour formuler par écrit ses observations. Passé ce délai, le relevé est censé être accepté par lui comme s'il était signé sans aucune réserve. Les relevés ne sont pris en compte, dans les conditions qui sont établies par la société B en vue des paiements, qu'autant qu'ils ont été admis par la société Z [1* p. 7].- If the structures are to be hidden or inaccessible later and, therefore, the volumes of work performed cannot be verified, company B must provide verification with the participation of company Z. If company Z considers that an amendment must be made to volumes provided by company B, these data are transferred to company B for approval. If company B refuses to sign these volumes, or signs with reservations, a protocol is drawn up outlining the arguments and circumstances. Company B is given a deadline of fifteen (15) days, starting from the day the arguments are voiced, for a written statement of their considerations. After this period, the statement is considered to be accepted without comment. Volumes issued by company B for payment are not taken into account, unless agreed with company Z [1 * p. 7].

Working with this passage showed that concordance is a useful feature in a situation where the TM does not contain incorrect translations and there is no glossary.

The function "subsegment search", "subsegment substitutions" is a search for inaccurate matches, for example, when the sentence to be translated differs from the one saved in the TM. For example, sentences that seem very similar are not so for a machine, or when a translated sentence is a combination of two previously translated. In this case, CAT analyzes TM, identifies stable correspondences at the level of word combinations in it and allows them to be substi-

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