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Artificial Intelligence (AI) applied in the legal field has gained considerable ground in the recent years and is used in many fields, amongst which the legal one is of no exception. This paper wishes to explore the quality of the translation (from English into Italian) of an arbitration clause performed by the ChatGPT chatbot. To do so, the automatically generated target text is post-edited by consulting the BoLC (Bononia Legal Corpus) and the web as corpus in the gov.it Italian governmental domain. General and legal dictionaries are also used. The paper findings report some inaccuracies at word level which are easily tackled by corpus consultation. In view of the results obtained, however, complete reliance upon AI-driven solutions is not recommendable at the time being. Conversely, the use of ad hoc corpora and of targeted web searches are the most feasible and reliable solutions, although time-consuming.

The use of Machine Translation (MT) and Neural Machine Translation (NMT) in professional translation activities is not only changing how translators work, but also altering how projects are managed and the expectations they entail within translation supply chains. The challenges posed by MT and NMT imply an increasingly involved role for translators in both company strategies and the managerial aspects of translation. This contribution aims to present the new professional opportunities and challenges in the translation industry considering the evolution of artificial intelligence, which has made automatic translation more precise and efficient, but has also raised concerns about the employability of human translators. Given the heightened complexity due to technological development and globalization, there might be a need for increased industry regulation, involving the establishment of new certification programs and the adoption of best practices to ensure the high quality of produced translations.

Translation technologies are widely used in the translation process and the market is witnessing a radical change in translation practices, with a significant impact on the translation professions. It is important that translation programmes adopted by universities consider the rapid advances in language and translation technologies and devote specific theoretical and practical courses to this topic. Students need to develop specific competences and skills and acquire an advanced mastery of the technologies. In particular, given the current dominant paradigm in machine translation, namely Neural Machine Translation, they need to be aware of the benefits but also the challenges it poses to translators. This paper discusses the concept of “human parity”, which has been proposed in the field as the main goal of current machine translation systems, and presents the teaching and learning of translation technology, with a particular focus on what has been developed at the University of Naples L'Orientale.

This short commentary focuses on language and law and legal translation. It stresses the importance of language used in legal texts in the legal context and the complexities when more than one language is involved in interlingual and cross-cultural communication in law. There are age old challenges in legal translation as well as new ones in the digital age with the increasing use of machine translation systems. It is imperative today that human legal translators are familiar with machine translation tools and aware of how computer aided translation technologies process information and their strengths and weaknesses. To be able to effectively use machine translation systems should become a compulsory part of the digital literacy and skill sets of legal translators in the twenty-first century.

The article is devoted to the analysis of the psychological scientific text machine translation (from the English language to the Russian one). On the material of the Russian machine translation of an informational brochure, potential difficulties were identified. The author points out the mistakes in machine translation and offers ways to solve them. A particular attention is paid to the translation of special terminology of the psychological semantic field. Background. Machine translation programs are often used as a way of getting texts translation. On the one hand, it is an available inexpensive way to get a quick translation. On the other hand, there is no guarantee of translation adequacy. The basis of quality translation analysis was a machine translation of scientific text. Purpose. On the basis of the text made by the machine translation program some mistakes are revealed and analyzed, some ways to solve them are offered. Special attention is paid to the translation of terms in the field of psychology. Materials and methods. The research material was a machine translation of the brochure “Anxiety Disorders in Children” written by the American Anxiety Disorders Association (the translation is from English into Russian). The translation was made with the help of the online program Google Translator. Results. Scientific texts on psychology have become popular in the world, but as the analysis showed, their translation is still difficult for online translation programs. Machine translation is not the way to get an equivalent text, but it does make the translation process easier. At the same time, an interpreter should spend time on further text editing. One type of mistakes is connected with the translation of terms. Besides there are mistakes caused by the difference between the grammatical structures of the original language and the translation language, such as the voice constructions, special word orders and set phrases.

The abstracts in Russian and in English are written according to certain rules. For articles written in Russian, an abstract in English is the only means of informing the world scientific community about the authors’ research results. However, the quality of abstracts written in Russian and then translated into English does not always meet the criteria for readability and comply with the accepted academic and publication conventions. This situation might result from the intensive usage of machine translation (MT) systems by authors who do not take into account the guidelines for the input text quality and the limitations inherent in MT systems. The author analyzed the requirements for the input texts and some typical errors in the target ones. The article describes the stages of training masters, postgraduate students and university staff in the effective use of MT systems. The training is based on a bilingual approach, which implies a constant comparison of vocabulary, grammar and style in the native and English languages. The author comes to the conclusion that the effective use of MT systems for writing an abstract in English is possible if the authors have the command of Russian and English at the level sufficient for a concise and unambiguous expression of their thoughts and ideas. Self-censoring is a prerequisite for creating a text that is “understandable” for MT systems. Students must follow simple rules: write sentences of 15-20 words; express one thought per one sentence; use more active verbs; choose nouns that express a specific concept; exclude unnecessary words. At the post-editing stage, learners can use the tools available on the Internet, which allow not only editing the target text, but also will enable learners to acquire independent editing skills.

Digitalization is one of the key distinctive features of modern environment and social life. Nowadays more and more functions are transferred to the artificial mind. How effective is the replacement of human activity with computer activity? In the given article, this problem is solved by an example of integration of digital technologies into translation activities. It this paper, emphasis is placed on the quality of machine translation (MT) output of legal texts in the language pair English - Slovak. It studies a Criminal Code formulated in the Slovak language which was translated by a human translator into English and consequently via machine translation system Google Translate (GT) back into Slovak. The back-translation - translation of a translated text back into its original language - as a quality assessment tool to detect discrepancies, mistranslations and inevitable differences between the source text and the target text was used. The quality of MT output was evaluated according to Multidimensional Quality Metrics (MQM) standards with the focus on the dimension of Fluency. The multiple comparisons were applied to determine which issues (errors) in Fluency dimension differ from the others. A statistically significant difference is noticed between Agreement and other issues, as well as between Ambiguity and other issues. The errors in Agreement are related to the differences between the languages: English is considered mostly an analytic language, Slovak represents a synthetic language. The issues in the Ambiguity dimension correlate with the type of the text being examined, since legal texts are characterized by relatively complicated wording and numerous terms; moreover, accuracy and unambiguity need to be preserved. Generally, the MT output is able to provide users with basic information about the text. On the other hand, most of the segments need revision and/or correction; in such cases, human intervention and post-editing is necessary.

This paper presents the post-editing features of the machine translation (MT) system Smartling used to translate the learning content of MOOC (Massive Open Online Course) Coursera. Most of the Coursera content is delivered in English, which is one of the reasons for the low uptake of these courses in Lithuania. With the growing demand for online resources, the need to translate courses into Lithuanian has become evident and MT systems are increasingly used for that purpose. This paper describes the results of an experiment carried out with the Smartling MT system. The experiment involved 10 participants, 6 professional and 4 non-professional translators, who post-edited a passage from the Coursera course The Science of Wellbeing. The post-editing process was monitored using the Translog-II tool, which captures the participants‘ keystrokes. The paper presents the classification and frequency of MT errors. One of the most important post-editing features of the Smartling MT system is the splitting of the text into subtitle lines, which is the cause of most grammatical errors. Among the errors not attributable to this text division are those caused by the polysemy of the words, literal translation and the use of pronouns. After the post-editing task, participants filled in a short questionnaire about the functionality of the Smartling system. 7 out of 10 participants rated the performance of this system as satisfactory. The results of the study showed that Smartling is not sufficiently tailored to the Lithuanian language, and that translators have to use a lot of cognitive effort in post-editing.

Kazakh language is related to languages with complex morphology and syntax. Today, most machine translation systems consider Kazakh language too, like Google, Yandex, Prompt, etc. This article describes the errors, shortcomings, and problems of machine translation (MT) into the Kazakh language. To analyze errors in machine translation into the Kazakh language, the most popular electronic translation programs were selected. When translating from Russian and English into Kazakh (and vice versa), various errors may occur, since the Kazakh language is dierent from other languages, and has special characteristics. To compare the results, an empirical method was used, namely, monitoring and testing the translation results of machine translation systems. Considering the results of statistical methods, the rule-based and neural networks based methods in machine translations were also analyzed. The practical signicance of the study lies in the development of recommendations for the identication and elimination of errors when editing the results of MT. The scientic signicance of the study lies in the fact that for the rst time errors and inaccuracies arising from machine translation of the Kazakh language have been systematized. The assessment of the quality of MT is also presented. The research carried out in this article will be used for the post-editing problem in machine translation.

Kanadalı yazar Lucy Maud Montgomery'nin bir romanı olan “Green Gables Anne” nin edebi çeviri ve makine çevirisi içeren bu çalışmada ele alınmıştır. Popüler kültür yayınlarından biri olan roman, başlangıçta sekiz kitaptan oluşan bir koleksiyonda anlatılan olayların bir özetidir. Bu makalede, çeviride insan faktörünün önemi üzerinde durularak, insan ve makine çevirisi arasındaki farklar incelenmiştir. Bu inceleme yapılırken, Google Çeviri ve Yandex Çeviri gibi çevrimiçi makine çevirisi uygu¬lamalarından yararlanılmıştır. Bu şekilde insan çevirisi ve makine çevirisinin farkları ele alınmış, insan faktörünün çevirideki önemi vurgulanmıştır. İnsan çevirmen tarafından yapılan son düzeltme işlemin¬de yazınların yazara ve eserin tamamına özgü özelliklerinin önemini ifade etmek amacındadır. Ayrıca bu ça¬lışma makine çevirisine yönelik güncel teknolojik çalışmalarda yazın çevirilerinin nasıl daha iyi yapılabileceği sorusuna cevap bulma amacı da taşımaktadır. Bu çalışmada, eserin yazın çevirisi ve bilgisayar donanımlı ma-kine çevirisi uygulamalarıyla yapılması üzerine bir değer¬lendirme yapılmıştır. Birçok yayın türünde olduğu gibi popüler kültür yayınlarının da makine çevirisiyle yapılabileceğini göstermek hedeflerdendir. Yapılan incelemede “Kural Tabanlı Makine Çevirisi” yöntemi tercih edilmiştir. Araştırmacının daha aktif olarak çalışmasına olanak tanıyan bu yöntemin diğer makine çevirisi türlerine göre daha bilimsel olduğu görülmüştür. Bunun yanı sıra Google Çeviri ve Yandex Çeviri gibi makine çevirisi sistemlerinin anlama özgü kavramları yansıtmaktaki eksiklikleri ve sadece kelime düzeyinde bir yapıyı temel alan bir sistemle çalıştıkları gözlemlenmiştir. Makine çevirisinin yaygınlaştığı günümüzde popüler kültür eserlerini içeren yazın çevirisinde insan müdahalesiyle etkin sonuçlara ulaşılabileceği görülmüştür. Bu süreçte son düzeltme işlemlerini gerçekleştiren çevirmenin anlayışı, kültürü, hayata bakış açısı ve yazın çevirisindeki bilgisinin önemine vurgu yapılmıştır. Özellikle yazın çevirisinde son düzeltme işleminin her zaman etkin sonuca ulaşmak demek olmadığı, her şeyin çevirmenin kültürü ve yazın çevirisi bilgisine bağlı olduğu verilen örneklerle tespit edilmiştir. Bu konuyla ilgili açıklamalarda bulunulmuş ve yazın çevirisinde çevirmen yorumunun önemi vurgulanmıştır.

The given paper views automatic translation process and the current state of translation market in different countries of the world. The author has analysed digitalization of translators and interpreters’ activity on international translation market, creation of translator’s workstation with computer making sophisticated multi-component working environment. The author underlines new IT translation technologies presuppose computer use as a tool in complex specialized translation software implementation. The author, as a practical translator, analyses the computer use in translation process, and translation and interpretation issues which is equal in its complexity for all countries in the world. The paper considers topical issues of machine translation, new economy, digital culture. It suggests some findings in streamlining translation process both in theRepublicofBelarusand in the world markets.

The article considers the issues related to the semantic, grammatical, stylistic and technical difficulties currently present in machine translation and compares its four main approaches: Rule-based (RBMT), Corpora-based (CBMT), Neural (NMT), and Hybrid (HMT). It also examines some “open systems”, which allow the correction or augmentation of content by the users themselves (“crowdsourced translation”). The authors of the article, native speakers presenting different countries (Russia, Greece, Malaysia, Japan and Serbia), tested the translation quality of the most representative phrases from the English, Russian, Greek, Malay and Japanese languages by using different machine translation systems: PROMT (RBMT), Yandex.Translate (HMT) and Google Translate (NMT). The test results presented by the authors show low “comprehension level” of semantic, linguistic and pragmatic contexts of translated texts, mistranslations of rare and culture-specific words, unnecessary translation of proper names, as well as a low rate of idiomatic phrase and metaphor recognition. It is argued that the development of machine translation requires incorporation of literal, conceptual, and content- and-contextual forms of meaning processing into text translation expansion of metaphor corpora and contextological dictionaries, and implementation of different types and styles of translation, which take into account gender peculiarities, specific dialects and idiolects of users. The problem of untranslatability (‘linguistic relativity’) of the concepts, unique to a particular culture, has been reviewed from the perspective of machine translation. It has also been shown, that the translation of booming Internet slang, where national languages merge with English, is almost impossible without human correction.

The paper is devoted to the research of the comparative analysis of the machine translation systems of French-language economic discourse in Ukrainian, taking into account the growing tendencies in the foreign economic activity of domestic enterprises and increasing the cooperation within the labor market. The comparative research covers the field of machine translation in terms of the development of computer linguistics and the growing demand for the translation services. The research focuses on the analysis of the translation of the French-Ukrainian language pair of economic discourse by using the existing machine translation systems. The history of the appearance of the first machine translation systems is analyzed. The authors identified the main approaches to the classification of machine translation systems, in particular: Fullyautomated machine translation, Human-assisted machine translation, Machineassisted human translation; Rule-Based Machine Translation, Statistical Machine Translation, Hybrid Machine Translation. The main peculiarities of which were named. The neural machine translation system as the one of most perfect type of machine translation system was analyzed at the current stage. A survey was conducted among the respondents (specialist-economists and scientists of economic sphere) regarding the most used machine translation systems. As a result, the following results were found: 61 respondents use Promt (39.1%), 48 respondents use Google Translate services (30.8%), 32 respondents use Yandex (20.5%), 13 respondents use other machine translation systems (8.3%) and only (1.3%) 2 respondents use the printed dictionaries and translate manually. Through the linguistic (at the morphological, lexical and syntactic levels) analysis, the advantages and disadvantages of machine translation by using the online systems Google Translate, Yandex and PROMT are revealed. Among the main advantages of machine translation are the following: speed; availability; fairly wide range of choice of equivalent matches, then, among the shortcomings, the following are noted: the literal translation; unpredictability in decision making and choice of equivalent; when translating, the machine translation system does not take into account a wide context. Besides the grammatical transformations at the word-formation and syntax levels are analyzed by using of the indicated machine translation systems. In addition, the authors’ translations were suggested. The causes of translation errors and the necessity to use the post-editing for improving the quality of the translation are determined.

The article presents the results of the research project funded by the Research Council of Lithuania, the aim of which was to find out the attitude of Lithuanian translation market participants – companies and freelance translators – towards machine translation and post-editing. Currently, there is no objective information on the application of machine translation in companies providing translation services in Lithuania or the attitude of translators to machine translated texts and the challenges posed by post-editing as a service. There is also no information on the competences that need to be developed by translators and translation students so that they can adapt to this new reality and work successfully on the market. To reach this aim, two questionnaires were prepared: one for translation companies providing translation services (n1=42) and the other for translators (n2=61), and two surveys were carried out. A qualitative analysis of survey data confirmed that among market participants there is a strong belief that as machine translation programs progress, the importance of competences ensuring quality post-editing will grow rapidly. Among the most important competences for post-editing, both groups of respondents singled out knowledge of text linguistics, native and foreign language proficiency, and knowledge of the field of translation. However, the surveys have also revealed that at least one third of market participants have a negative view toward machine translation and see it as a threat to the quality of translation (agencies) and/or their jobs (translators). It is expected that the conducted research will contribute to the promotion of a more favourable attitude of translators to machine translation technologies and will encourage further research in post-editing in Lithuania.

With the advancement of mobile applications, now it is possible to perform instant text translation using a smartphone’s camera. Because text translation within images is still a relatively new field of research, it is not surprising that the translation quality of these mobile applications is under-researched. This study aims to determine the image-to-text translation quality in the English to Lithuanian language direction using popular machine translation apps. To classify errors and evaluate the quality of translation, the present study adopts and customizes the Multidimensional Quality Metrics (MQM) framework (Lommel 2014). The obtained results indicate that image-to-text machine translation apps produce exceptionally low-quality translations for the English-Lithuanian language pair. Therefore, the quality of machine translation for low-resource languages such as Lithuanian remains an issue.

The language policy and planning in the PRC are represented on various official central and local websites. The annual statistical analysis of Chinese words and characters used online are among the tasks, set by the authorities before the linguists. The technologies, allowing to recognize words within the character texts and to mark them as belonging to a particular part of speech in the isolating syllabic Chinese language are applied within the process of creating numerous text corpora, often accessible on the Web. Speech recognition and speech synthesis technologies based on AI are used in machine translation, on the official websites for Standard Mandarin learners, as well as in spoken corpora. Among the latter are dialect corpora created in Taiwan and Hong Kong and available online

Some weaknesses of machine translation carried out by means of neural networks (“neural translation”) are considered. The relevance of this topic is determined by the significant popularity of the relevant web services among translators, teachers, researchers, students and others who are interested in the introductory or urgent transcoding of the text from one language to another. The results of the experiment are presented: well-known web service “Google Translate” was offered to translate into English several dozens of Russian-language terms and concepts related to philology and pedagogy. Recommendations are given to correct some errors and inaccuracies made by the web service. The authors come to the conclusion that the use of neural translation has led to a significant improvement in the quality of services provided by the web service “Google Translate.” It is noted that this service still makes mistakes (semantic and stylistic) when translating the following categories of vocabulary: compound terms and concepts; lexical units and phrases that do not have clear equivalents in the English language; two words with the same English equivalent; abbreviations; author’s occasionalisms. It is reported that, according to the authors, such errors are made by other popular web services (“Yandex.Translator,” “Translate.ru” etc.)

This paper provides a broad overview of issues related to the construction of finite state transducers with one state for the two-level morphology of inflectional languages, particularly, the direct transformation of word endings to the grammatical characteristics. This problem has been studied on the base of the Kazakh and Russian languages, which are usually named the inflectional languages. The solution of this problem is the trivial Mealy automaton with one state, i.e. a single state transducer, and a multi-valued mapping method is used as well. We study the problem of completeness of the finite state transducers input for the analyzed languages. The determination of transducer input completeness for morphological analysis gives a guarantee that all the words of the analyzed language will be accepted. The problem of determining the completeness of the set of possible endings for agglutinative languages is a complex issue. In this article, we define the completeness of a set of endings in Kazakh language. The proposed technology is implemented for the Russian-Kazakh machine translation, a translation quality assessment performed by the method of BLEU.

This article describes computational linguistics briefly, and explains Turkic language studies in this field using Uyghur language as an example. With developing computer technologies, many software has been implemented in order to complete some tasks in place of human. For example, translate from one language to another, or translate from one language to more than one languages at the same time, correcting or editing texts, analyzing documents, converting speeches into texts or converting texts into speeches etc. Until now, there are many successful researches have been done on different languages such as English, Japanese, Arabic, Turkish, Chinese, French and Russian etc. In Turkic languages, especially in Turkey Turkish, though there are some important researches have been done, other Turkic languages still at a beginning stage. Though, Uyghur language belongs to Turkic language family and it has common properties with other languages, however research results about other Turkic languages cannot be applied to Uyghur language directly. As a natural language, Uyghur language has many special properties those (are) different from other Turkic languages. This paper summarizes some computer based researches about Uyghur language and use them as a part of general machine translation system of the Turkic world.

This paper investigates how different features influence the translation quality of a Russian-English neural machine translation system. All the trained translation models are based on the OpenNMT-py system and share the state-of-the-art Transformer architecture. The majority of the models use the Yandex English-Russian parallel corpus as training data. The BLEU score on the test data of the WMT18 news translation task is used as the main measure of performance. In total, five different features are tested: tokenization, lowercase, the use of BPE (byte-pair encoding), the source of BPE, and the training corpus. The study shows that the use of tokenization and BPE seems to give considerable advantage while lowercase impacts the result insignificantly. As to the BPE vocabulary source, the use of bigger monolingual corpora such as News Crawl as opposed to the training corpus may provide a greater advantage. The thematic correspondence of the training and test data proved to be crucial. Quite high scores of the models so far may be attributed to the fact that both the Yandex parallel corpus and the WMT18 test set consist largely of news texts. At the same time, the models trained on the Open Subtitles parallel corpus show a substantially lower score on the WMT18 test set, and one comparable to the other models on a subset of Open Subtitles corpus not used in training. The expert evaluation of the two highest-scoring models showed that neither excels current Google Translate. The paper also provides an error classification, the most common errors being the wrong translation of proper names and polysemantic words.

Machine translation (MT) is a relatively new field of science. MT systems are evolving in certain directions. The article discusses the possibilities and the future of systems currently offered to public by the biggest technological companies focusing on English-Russian translation relations.

In this paper we investigate the technique of extending the Moses Statistical Machine Translation (SMT) system default set of features using shallow linguistic information from source and target phrases. Although a typical SMT system uses a phrase table with 5 default features, most systems are scalable and support any number of additional features. We assume that linguistic information extracted from the source and target phrases can improve the overall translation quality, i. e. make the system more robust and reduce the number of instances of incorrect word choice, punctuation mistakes and other problems SMT systems are prone to. First, we build a baseline SMT system. Then we extract shallow linguistic features directly from source and target phrases of the baseline system’s phrase table. The features are precomputed and stored in the phrase table, so they can be regarded as stateless dense features. We develop and examine 19 features incorporating information from source and target phrases. We explore features commonly used in monolingual and parallel data filtering techniques. The features we investigate include source and target phrase lengths, word, number and punctuation symbol count, word frequencies according to large monolingual corpora etc. For each feature, we build and evaluate a separate SMT system. We conduct a series of experiments on the English-Russian language pair and obtain statistically significant improvements of up to 0.4 BLEU compared to baseline configuration.

In this research we concentrate on how CSP techniques can contribute to computational linguistics. For this purpose, we tested CP techniques on a linguistic problem of polysemy in machine translation. Ambiguity arises almost every time computers try to deal with human language. In information retrieval, the computer often provides information about alternative meanings of the search terms, meanings that we have no interest in. In machine translation, different meanings of an English word may be expressed by very different words in the target language, so it is important to determine which meaning of the English word the author had in mind and this is where a computer fails. Over and over, attempts to use computers to process human language have been frustrated by the computer’s limited ability to deal with polysemy (Miller 2001). A toy system has been designed for translating between English and Greek. It is based on 6 sentences that, using the same lexicon, can be expanded to slightly more phrases. The translation is based on the results of two parsers — the English and the Greek one. Parsing uses a set of constraints (both semantic and grammatical). The main objective of this research is to evaluate Backtracking as one of CSP algorithms for machine translation. We used PROLOG to write an application because it is the only programming language that supports backtracking and where the choice made by the program can be traced back at every stage.

The article considers two machine translation systems, PROMT and Google Translate, which work on different principles. PROMT works on the so-called "translation by the rules" principle. Google Translate uses the technology which is based on the statistics principle. The article presents the linguistic analysis of the quality of the translations of text fragments of various genres made from English into Russian by these systems. The most typical mistakes made by the systems are revealed, the possibilities and spheres of use are determined. The author reveals that the systems are not useful for translating texts which are full of compound and complex sentences. The programs work mainly on the phrase level and can be fruitfully used for translating texts of formal style, for example, technical documents, manuals, etc., which contain simple extended sentences and do not contain sentences with complex syntactic constructions. The author considers that professional translators can use these systems while working with certain text types; however, the translations made by the systems must be checked thoroughly and verified.

The article demonstrates the role of machine translation programs as artificially developped systems imitating human mental activity and their influence on human personality in the 21st century.

The aim of this research project is to verify whether machine translation (MT) technology can be utilized in the process of professional translation. The genre to be tested in this study is a legal contract. It is a non-literary text, with a high rate of repeatable phrases, predictable lexis, culture-bound terms and syntactically complex sentences (Šarčević 2000, Berezowski 2008). The subject of this study is MT software available on the market that supports the English-Polish language pair: Google MT and Microsoft MT. During the experiment, the process of post-editing of MT raw output was recorded and then analysed in order to retrieve the following data: (i) number of errors in MT raw output, (ii) types of errors (syntactic, grammatical, lexical) and their frequency, (iii) degree of fidelity to the original text (frequency of meaning omissions and meaning distortions), (iv) time devoted to the editing process of the MT raw output. The research results should help translators make an informed decision whether they would like to invite MT into their work environment.

It is discussed in this article the role of editing in translating process. On the one hand, though a translator is considered to be «alone fighter» in his activities, it is still impossible to keep all the extensive information in one mind. On the other hand, despite the rapid successful development of machine translation systems they have one considerable disadvantage - lack of feeling for language, without that the proper translation cannot be achieved. So for improving the linguistic quality of the translation, both traditional and computer, editing is required. Some means of editing are defined as well in the present article.