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Introduction
The use of modern information technologies in the formation of translation skills allows solving such tasks of the educational process as activation of the student's educational activity, implementation of individual training, saving of educational time, controllability of results, use of the best world pedagogical experience, creation of conditions for the practical application of knowledge and skills (Baar, 2012). Information technologies help to effectively implement such didactic principles of linguistic education as scientificity, accessibility, visibility, and autonomy (Cem & Kokturk, 2015).
A special place among information technologies in the professional activity of a translator is occupied by electronic text editors, which significantly simplify the editing process and improve the quality of translation (Bowker, 2015).
The elimination of spelling, punctuation, grammar, and lexical errors in research on natural language processing (NLP) (Pym, 2011) is called "grammar error correction" (GEC) (Allen, 2003).
Electronic text editors are systems that aim to solve a GEC problem, receive erroneous suggestions, and issue a revised version. According to researchers, electronic text editors are software that helps a translator to perform better translation: check his/her translation for grammar errors (Declercq, 2015), and whether a word or a fragment has been released in the translation; whether the unity of style is preserved (Sharif & Rabbani, 2016; Usmanova et al., 2021); whether the same word or expression is repeated too often; are the combinations of words, subordinate clauses correct; are all tenses and cases consistent and the like (Cavaleri & Dianati, 2016).
The importance of mastering the skills of using electronic text editors is convincingly evidenced by the fact that the translation course in the world university practice of education necessarily provides training in the use of these tools since universities strive to provide students with the necessary and relevant professional skills (Pym, 2013). Active scientific work is being carried out in the world to develop the foundations for the creation of teaching methods for these programs and the actual development of such methods (Ghufron & Rosyida, 2018).
Currently, the problem of automatic editing is attracting more and more attention of specialists in the field of philological sciences, since improving the stylistic and grammar skills of future translators with the help of electronic text editors will increase the competitiveness of a specialist translator in the translation services market.
The opinions of researchers on the advantages of electronic text editors are presented in Table 1.
Table 1 - The main advantages of electronic text editors.
| Source | The main advantages of electronic text editors |
|---|---|
| Risku (2013) | eliminate grammatical and punctuation errors, optimize vocabulary and improve the structure of sentences |
| Sousa-Silva (2014) | many electronic text editors include means of checking for plagiarism |
| Marecek, et al (2011) | ease of editing and proofreading, which helps to optimize the work that occurs after the translation |
| Doherty (2016) | a tool for writing and editing all types of content, including blog articles, books, and social media posts |
Researchers Parton et al. (2012); Toncic (2020); Basmanova et al. (2020), note that today, there are many different services (Grammarly, ProWritingAid, LanguageTool, Ginger, SlickWrite, Hemingway, Readable, Sapling, Paper Rater, etc.) that help students check their work for stylistic, grammar, spelling, and punctuation errors.
Research hypothesis: the use of electronic text editors is an effective means of improving the stylistic and grammar skills of future translators.
Research objectives:
to carry out a comparative analysis of the capabilities of existing text editors to improve the stylistic and grammar skills of future translators,
to rank the most popular text editors and select a text editor for conducting an experimental study.
to select students of the experimental and control groups to perform text translation;
to determine the error evaluation procedure and check the edited texts based on the described evaluation procedure;
to carry out quantitative processing of experimental data and to carry out their interpretation;
to formulate conclusions and prospects for further research.
The article consists of an introduction, literature review, methods, results, discussion, and conclusion.
Materials and methods
Research design
To achieve this goal, we conducted a study using qualitative and quantitative methods to assess the capabilities of existing text editors to determine the possibilities of improving the stylistic and grammar skills of future translators. Therewith, a set of theoretical and empirical research methods was used:
theoretical methods (analysis, synthesis, comparison, generalization) - for the study of literary sources related to the research problem;
empirical methods (the expert survey method, a pedagogical experiment on the use of a text editor to improve the stylistic and grammar skills of future translators);
numerical methods (the method of mathematical processing of respondents' answers, the ranking method, the method of mathematical processing of the results of a pedagogical experiment).
In total, 21 sources of information necessary for the implementation of the research goal were selected. The first group of sources: monographs, articles published in journals indexed by Scopus and Web of Science, containing conceptual provisions regarding the use of electronic text editors. The second group of sources: articles published in journals indexed by Scopus and Web of Science, and speeches at conferences of researchers from different countries devoted to the experience of using electronic editors to improve the stylistic and grammar skills of students.
The main research methods were the survey method and the method of pedagogical experiment.
During the online survey (January 2021), English language teachers representing five Russian universities had been interviewed, a total of 36 experts. The criteria for selecting experts were whether they had articles on this topic published in journals included in the Scopus or Web of Science citation databases in the amount of at least three or at least eight years of work experience. All the survey participants were warned about the purpose of the survey and that the organizers of the study planned to publish the results of the study in the future without specifying the personal data of the participants of the expert survey.
To implement the pedagogical experiment, we recruited two groups of students of the translation department of the Faculty of Foreign Languages (39 people) who were studying in the third year of the bachelor's educational level.
The students of the experimental group (EG, 20 people) had the opportunity to use an electronic text editor in the process of proofreading the test translated from Russian into English. Students of the control group (CG, 19 people) carried out post-translation editing without using an electronic text editor.
Research tools, procedure
The experts were asked questions about the most popular English-language electronic text editors. Then, based on the selected criteria (availability of a free version, ease of use, grammar and spelling correction, multilingualism, style correction, compatibility with various browsers and OS, plagiarism checking), the experts ranked them to determine the optimal option for improving the stylistic and grammar skills of future translators.
The experts were sent electronic messages with a request to indicate the most popular, in their opinion, electronic text editors used in the implementation of the translation, as well as to rank them.
The experts were sent the same request in Russian at the same time and were given 20 calendar days to be able to respond. A limited period and a one-time dispatch made it possible to establish equal conditions for experts.
We assumed to choose an electronic text editor that received the highest rank according to the results of an expert survey to test the hypothesis of the study.
We settled on an English-language text with a volume of 24.5 thousand printed characters without spaces to conduct an experimental study. We created our translation of the text from Russian to English using the Google Translate translation automation system and offered it as a text for post-translation editing. To understand the research methodology, we consider it necessary to explain that in our study we proceeded from the position that pre-editing should not be confused with post-editing with the use of electronic text editors, which concerns the preparation of text for processing by a software product for automatic translation. Preliminary editing in our study was explained to students as viewing the content of the text to identify the main errors, marking certain content for translation in one way or another (or as untranslatable), and optimizing formatting (Pym, 2011). This preparation of the text for its processing using translation can significantly improve the final result, therefore it is considered an important stage of the automatic translation process. Effective pre-editing of the text can significantly improve the quality of the source text of the translation, simplify the work of both machine translation systems and the post-editing stage.
Mathematical processing of research results
The ranking of electronic text editors consists in their arrangement by each of the experts in the form of a sequence according to their decreasing preference. Therewith, each of the electronic text editors is evaluated by the rank (number) under which it is located in this sequence. The final rank of the electronic text editor is the arithmetic mean of all expert ranks in the sample of experts. When processing the results of the study, the Microsoft Excel application was used.
When choosing a system for evaluating post-translation editing, we were guided by such criteria as the number of stylistic errors and the number of grammar errors (word-formation, morphological, syntactic). One penalty point was awarded for each mistake made.
Student's t-test for independent samples was used to assess the reliability of the differences in the results of post-editing in EG and CG.
Results and discussion
Based on an expert survey, the most popular electronic text editors were identified, their ranking was carried out based on the selected criteria (Table 2).
Table 2 - Ranking of electronic text editors.
| No | Editor | Comparison criteria | Rank | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Free version | Ease of use | Grammar and spelling correction | Multilingualism | Style check | Compatible with various browsers and OS | Checking for plagiarism | |||
| 1 | Grammarly | + | + | + | - | + | + | + | 1 |
| 2 | Ginger | + | + | + | + | + | + | - | 2 |
| 3 | Whitesmoke | - | - | + | + | + | + | + | 3-4 |
| 4 | LanguageTool | + | - | + | + | + | + | - | 3-4 |
| 5 | ProWritingAid | + | - | + | - | + | + | - | 5 |
| 6 | Hemingway | + | + | - | - | + | + | - | 6 |
| 7 | PaperRater | + | - | + | - | + | + | + | 7 |
| 8 | Slick Write | + | + | - | - | + | + | - | 8-9 |
| 9 | Proofreading Tool | + | + | + | - | + | - | - | 8-9 |
Table 3 shows the results of calculating penalty points in the text of the translation (from Russian into English), the editing of which was carried out by students with and without the use of the electronic text editor Grammarly.
Table 3 - Comparative results of performing post-translation editing by students of EG and CG.
| Post-translation editing using Grammarly (in penalty points, the average for the group) | Post-translation editing without using Grammarly (in penalty points, the average for the group) | t emp | |
|---|---|---|---|
| Stylistic errors | 23.5 | 44.3 | 16.24 |
| Grammar errors, including | 40.0 | 54.5 | 4.688 |
| word-formative | 12.5 | 16.3 | 4.345 |
| morphological | 13.5 | 18.9 | 5.123 |
| syntactic | 14.0 | 19.3 | 4.756 |
| TOTAL | 63.5 | 98.8 | 10.53 |
As follows from Table 3, the editing of the translation performed by students without using Grammarly was 1.54 times worse than the editing of the translation performed by students using Grammarly. Moreover, the tendency to a lower level of quality is observed within the limits of both stylistic errors and all types of grammar errors: spelling, syntactic, punctuation.
It should be noted that if the number of grammar errors among students who did not use an electronic text editor in post-translation editing exceeds the number of grammar errors among students who used it in their work by 1.36 times, then the number of stylistic errors is 1.89 times higher.
Thus, it follows from Table 3 that the EG students coped much better with editing the English translation text provided to them, especially with the stylistics of the text. Calculation of Student's t-test for independent samples (Table. 3) also showed statistically significant differences between the two groups in all the assessed indicators of the quality of post-translation editing (ttest= 2.695; p < 0.01).
Therefore, it can be assumed that a significant role in reducing the number of stylistic and grammar errors during editing was played by the electronic text editor Grammarly, which was used by EG students during editing.
Comparing our results with the results of similar studies, we can generalize that despite certain difficulties, the use of electronic text editors expands the possibilities of post-translation editing, makes the editing procedure more efficient, and has several advantages over traditional editing. First of all, it is the acceleration and automation of the editing process (Risku, 2013). Another very significant advantage of using electronic text editors is the possibility of budget editing due to free services.
Such subjective components as a clear user interface, as well as the availability of a mobile application available on iOS and Android, are no less important for future translators when choosing an electronic text editor (Doherty, 2016).
In general, according to the researchers Marecek et al. (2011), post-editing involves improving the text obtained in the translation process, including machine translation, by a translator who usually undergoes special training. The amount of time and labor required for post-editing is one of the key factors that should be taken into account when evaluating the economic efficiency of machine translation (Cavaleri & Dianati, 2016). Sometimes, to ensure the proper quality of the text, close to human translation, it may be necessary to rewrite the text again (Cem & Kokturk, 2015).
As noted earlier, to improve the quality of machine translation and simplify the post-editing stage, there is a need for preliminary editing of the text, which should be carried out taking into account the following main recommendations.
Thus, researchers (Declercq, 2015) suggest checking the source text for typos and spelling errors, since a computer translator is not able to recognize incorrectly typed words; track missing or, conversely, unnecessary punctuation marks that may prevent an electronic translator from correctly understanding the syntactic structure of a sentence; and also take into account the adequacy of the use of diacritics and the case (layout) of letters when pre-editing.
The study (Marecek et al., 2011) suggests the need to remove words from the source text that do not expand the semantic content of the sentence, as well as the desirability of replacing pronouns with corresponding nouns, which contributes to an adequate "perception" of the content of sentences by translation programs. According to Pym (2011), slang expressions, cliches, idioms, and colloquial phrases should be avoided in the source text for translation, since machine translation tools can incorrectly (often literally) convey their content and the text will not make sense in the target language. The study (Parton et al., 2012) provides reasoned recommendations on the use of simple syntactic constructions with direct word order for translation, avoiding complex sentences and homogeneous parts of a sentence when each sentence should convey one logical thought, which is equally applicable to all languages when translated and is the most effective.
Following these recommendations can greatly simplify the post-editing of the text in the target language.
Conclusions
The formation of stylistic and grammar skills of future translators means the practical mastery of the structural phenomena of the language by students and should occur in close cooperation with the formation of language and speech skills against the background of positive interests and motives of students.
Today, translation activities are becoming more and more popular given the rapid development of information technologies and the steady growth of the volume of information. As a result of technological progress, there has been an increase in the requirements for the training of future translators and the quality of their translations, i.e., to the peculiarities of using electronic text editors in the conditions of their professional training. Therefore, the training of future translators in the use of electronic text editors is becoming increasingly important and becomes very relevant.
As the results of the pedagogical experiment showed, the translation editing performed by students without using an electronic text editor turned out to be worse than the translation editing performed by students using an electronic text editor. Therewith, the greatest differences are recorded within the limits of stylistic errors.
Thus, the hypothesis of the study that the use of electronic text editors is an effective means of improving the stylistic and grammar skills of future translators was confirmed.
We see the prospect of further research in the expansion of the experimental base (more students and translations of a larger text).
