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Impact of computer-assisted translation tools by novice translators on the quality of written translations

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The main specificity of the modern translation market is the translation of large volumes of technical texts and business documents in the shortest time possible. The purpose of the study is to conduct an experiment on the impact of machine translation systems (in terms of using term bases) on the efficiency of future translators. The study provides a literature review on the problem under study and presents the advantages of computer-assisted translation tools in translation practice. Based on the experimental study, the analysis of the influence of computer-assisted translation tools on the quality of written translations of student translators was carried out.
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Laplage em Revista (International), vol.7, n. Extra C, 2021, p.714-721
ISSN: 2446-6220
IMPACT OF COMPUTER-ASSISTED TRANSLATION TOOLS BY
NOVICE TRANSLATORS ON THE QUALITY OF WRITTEN
TRANSLATIONS
INTRODUCTION
The relevance of the study is due to the
increasing importance of ICT in the modern
translation service market. Traditional ideas
about translation as a professional activity are
changing, and the professional model of a
translator’s professional activity becomes
more complex. Scientific and technological
progress undoubtedly influences translation
activity.
The latest technology often determines the
types of text to be translated. In addition to the
demand for translation of texts in traditional
industries, for example, economic or technical,
new areas are constantly emerging, in which
the volume of translations is increasing. A
modern translator masters a certain industry
(or several branches) in which they specialize
and must constantly replenish their
knowledge in the chosen field study not only
new terminology but also modern software,
used in the translation process and constantly
updated and improved (RISKU, 2013).
A translator must be competent in many
aspects, and ability to work with specialized
software is extremely important in the implementation of translation activities. A special place
among ICT in the professional activity of a translator is occupied by computer-assisted
translation (CAT) tools, which greatly simplify and speed up the translation process (HUANG
et al., 2013). In the translation industry, machine translation systems are widely used by both
large translation companies and freelance translators. According to the estimates of the
PROMT company, use of CAT tools can increase the efficiency of translations up to 80%
(ALCINA, 2008).
However, the ability to use CAT tools is not enough to perform a quality translation. The
translator must know the vocabulary and terms specific to a particular industry, which will
significantly improve the quality of the final result. In the absence of the necessary knowledge,
editing a text translated with a CAT program becomes more difficult since the translator is
somewhat disoriented (LI, ZHANG, 2010).
LITERATURE REVIEW
According to researchers, machine translation systems, or CAT tools (computer-assisted
translation, computer-aided translation), are software that help to translate faster (GARCIA,
2014). The importance of mastering the skills of using machine translation systems is
convincingly evidenced by the fact that a translation course in European or American
universities necessarily provides for teaching the use of CAT tools since higher education
institutions strive to provide students with the necessary and relevant professional skills
(ZHONG, 2010). Abroad, active scientific work is carried out to develop foundations for
creating teaching methods for these programs (YANFU, 2010; ROSTOVSKAYA et al., 2020;
VINICHENKO et al., 2019) and developing such methods (SONG, ZHANG, WANG, 2010;
DYGANOVA, YAVGILDINA, 2020; OBEDKOVA et al., 2020). Researchers’ opinions on the
benefits of CAT tools are presented in Table 1.
AUTHORSHIP
Zulfiya Akhatovna Usmanova
Рeoples Friendship University of Russia (RUDN University), Moscow,
Russia.
ORCID: https://orcid.org/0000-0002-4410-9089
E-mail: usmanova_za@pfur.ru
Ekaterina Nikolayevna Zudilova
Moscow Aviation Institute, Moscow, Russia.
ORCID: https://orcid.org/0000-0003-0974-4359
E-mail: katarinocka@yandex.ru
Pavel Alekseevich Arkatov
Moscow Polytechnic University, Mosco w, Russia.
ORCID: https://orcid.org/0000-0003-2267-4148
E-mail: arkatov.p.a@mail.ru
Nataliaya Grigorievna Vitkovskaya
Russian State Social University, Moscow, Russia.
ORCID: https://orcid.org/0000-0003-1277-9616
E-mail: natashavit@rambler.ru
Ekaterina Vladimirovna Kravets
Technological University, Korolyev, Moscow Region, Russia.
ORCID: https://orcid.org/0000-0002-1405-6983
E-mail: kravets.e.v@mail.ru
Approved in:
2021-07-20
DOI: https://doi.org/10.24115/S2446-622020217Extra-C1154p.714-721
Zulfiya A. Usmanova; Ekaterina N. Zudilova; Pavel A. Arkatov; Nataliaya G. Vitkovskaya; Ekaterina V. Kravets
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Table 1. Key benefits of CAT tools
Source
Key benefits of CAT
Barrachina S. et al.
(2009)
ability to reuse translations, CAT systems automatically perform a search in memory and offer
reuse of found matches, not only at the level of sentences but also at the level of phrases and
individual words, which is useful in texts containing repetitions of phrases
Krüger R. (2016)
dictionary autosearch: CAT tools highlight terms in the text that are known to the terminology
management component
Vargas-Sierra C.
(2019)
check for completeness of translation: CAT systems do not allow skipping untranslated
segments, which greatly simplifies the work
Dragsted B. (2006)
ease of editing and proofreading: use of CAT tools by editors and proofreaders helps to
optimize the work that occurs after translation
Bing, X., Hongmei,
G., Xiaoli, G (2007)
optimization of joint activities: ensuring stylistic and terminological uniformity through the joint
use of the CAT system by the translation team
Dillon S., Fraser J.
(2006)
improved translation quality control: several CAT systems contain translation quality control
modules; there are also specialized quality control programs that perform an automatic
comparative analysis to identify errors in terminology, translation inconsistencies, etc.
Source: Search data.
However, according to scientific research, when translating, students mainly focus on the
superficial structure of the original text (OT), while professional translators analyze the meaning
of the OT and try to recreate the essence of the original message. When starting to translate
texts on various subjects in CAT programs, they trust the machine and do not pay attention to
some mistakes made during translation (CHUNZHI, 2014).
In this regard, we conducted an experiment that would show how much novice translators rely
on machine translation systems and whether they have a correct level of vocabulary that is
typical for texts on a certain subject. The purpose of the study is to conduct an experiment on
the impact of machine translation systems (in terms of using termbases) on the efficiency of
future translators. Implementation of formulated purpose requires the solution of several tasks:
to select participants of the experiment; select the OT; compose a term base for OT,
which contains errors;
to organize the translation of OT and divide participants into two groups where one
group uses the proposed termbase and the other does not;
to determine the principles by which translation texts will be analyzed and analyze
them, based on these principles;
to carry out quantitative processing, analysis, and interpretation of the data obtained;
to formulate conclusions and prospects for further research based on the results.
We formulated the following hypotheses of the study.
Hypothesis 1. When translating the OT with a CAT tool using a termbase with erroneously
translated lexical units, the quality of student translation will decrease.
Hypothesis 2: When translating the OT with a CAT tool without using a termbase with
erroneously translated lexical units, the quality of student translation will increase.
The study consists of an introduction, literature review, methods, results, their discussion, and
conclusion.
METHODS
The material of the study is 24 texts of the translation of an article on sociopolitical topics,
translated using CAT tools by novice translators. The study purpose and objectives led to the
use of several methods, including theoretical: analysis, generalization, and systematization of
data from Russian and foreign works on translation studies, methods of teaching translation,
current trends in the translation services market, ICT tools for translators. We used system-
structural analysis and synthesis to compare the available range of machine translation systems
and establish the best for conducting an experimental, as well as empirical study, determining
the effect of CAT tools on the quality of the translation of texts. We used quantitative methods
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for processing experimental data. The experimental study on the impact of machine translation
systems (in terms of the use of termbases) on the quality of written translations of novice
translators was carried out in three stages:
preparatory stage, during which hypotheses were formulated and an experimental
plan was developed,
main stage, which consisted of the practical implementation of the experiment and
final stage, which provided for the analysis and interpretation of experimental data.
To test the hypotheses, we selected 26 students of the translation department studying French,
divided into two experimental groups (EG-1 and EG-2), including 6 men and 20 women aged
21-22 years. The experimental study was carried out in the course of teaching the basics of
working with CAT tools within the discipline “Translation Practice”. Students were acquainted
with the basics of working in machine translation systems. They translated texts from French
into Russian and vice versa, using machine translation module, translation memory bases, and
termbases. As an OT, an excerpt of an article in French on sociopolitical topics from a French
online magazine was borrowed.
The text consists of 14 sentences, 373 words, 2,288 printed characters (with spaces). It was
shortened so that the students could complete the translation in class in 80 minutes. During
the translation, the students used the cloud-based machine translation system Memsource, as
well as a termbase compiled specifically for the experiment, which contained translation errors
for 26 lexical units. All students fully translated the text into Russian. Students of the first
experimental group (EG-1) used a termbase with errors in the process of translating, while
students of the second experimental group (EG-2) relied only on their knowledge. When
checking student translations, we relied on the following grading system:
1) errors in which the content of the OT is significantly distorted and/or wrong translation
option is chosen, which is proposed in the termbase (1 penalty point is charged);
2) errors in which the meaning of the OT is partially lost and/or translation offered in the
database is partially selected (0.5 penalty points are charged);
3) errors that insignificantly or hardly affect the maintenance of the OT (0.1 penalty point is
charged).
RESULTS
The results of the experimental section of the EG-1 group are shown in Table 2.
Table 2. Results of the experimental section performed by the EG-1 group using the termbase
with errors (in penalty points)
Student
Type 1 errors
Type 2 errors
Type 3 errors
Total number of
penalty points
Student 1
8.0
1.0
0.1
9.1
Student 2
9.0
0.5
0.2
9.7
Student 3
13.0
1.5
0.0
14.5
Student 4
6.0
0.0
0.2
6.2
Student 5
5.0
1.0
0.2
6.2
Student 6
5.0
0.5
0.2
5.7
Student 7
3.0
3.0
0.2
6.2
Student 8
7.0
1.5
0.1
8.6
Student 9
9.0
1.0
0.0
10.0
Student 10
8.0
0.0
0.1
8.1
Student 11
4.0
0.5
0.3
4.8
Student 12
7.0
0.5
0.1
7.6
Average value
7.0
0.9
0.1
8.1
Source: Search data.
As follows from Table 2, in the group, the average value of the penalty score was 8.1, while the
average value of errors of the first type, to which we attribute a significant distortion of the
Zulfiya A. Usmanova; Ekaterina N. Zudilova; Pavel A. Arkatov; Nataliaya G. Vitkovskaya; Ekaterina V. Kravets
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content or the choice of a certain word or phrase proposed by the termbase, was 7.0. The
indicator of errors of the first type varied from 3.0 to 13.0 penalty points. The high score for
errors of the first type in EG-1 is explained by the fact that the students were mainly guided by
the base, rather than their knowledge and context.
The average penalty point for errors of the second type was 0.9, which can be explained by
the fact that lexical units, the content of which was slightly changed, were generally familiar to
the students. The maximum penalty point was 3.0, which can be explained by the fact that
some lexical units were unknown to the students, in contrast to other subjects. The average
penalty point for errors of the third type, which includes those that do not affect the content of
the message, in EG-1 was 0.1 (with the highest indicator being 0.3 penalty points).
Thus, we conclude that, most likely, the students who used a termbase with translation errors
relied on the version proposed in it, instead of a critical analysis of the resources provided,
thoughtful analysis of the meaning of the original message, and an attempt to reproduce its
essence. Table 3 presents the results of the experimental section of the students from the EG-
2 group, which did not use the termbase with errors.
Table 3. Results of the experimental section performed by the EG-1 group without using the
termbase with errors (in penalty points)
Student
Type 1 errors
Type 2 errors
Type 3 errors
Total number of
penalty points
Student 1
4.0
0.5
0.3
4.8
Student 2
7.0
0.5
0.1
7.6
Student 3
2.0
2.0
0.1
4.1
Student 4
4.0
2.0
0.2
6.2
Student 5
2.0
1.5
0.1
3.6
Student 6
3.0
2.0
0.0
5.0
Student 7
4.0
1.0
0.1
5.1
Student 8
5.0
2.0
0.1
7.1
Student 9
5.0
2.5
0.2
7.7
Student 10
5.0
1.5
0.1
6.6
Student 11
2.0
2.5
0.2
4.7
Student 12
4.0
0.5
0.3
4.8
Student 13
7.0
1.5
0.1
8.6
Student 14
11.0
1.0
0.1
13.1
Average value
4.6
1.8
0.2
6.4
Source: Search data.
According to the data presented in Table 3, the total penalty point was 6.4, which was less than
the same indicator in the EG-1 group. Group total scores ranged from 3.6 to 13.1 penalty
points.
In EG-2, the students also made mistakes of the first type, but there were much fewer of them
than mistakes of the second and third types. The average indicator of errors of the first type
was 4.6, which was much less than the same indicator in EG-1. The highest penalty score (11)
most likely indicates that lexical units were unknown to the students and, therefore, caused
certain difficulties in translation. Three students made only two mistakes of the first type, which
was the best result in the group. Interestingly, the indicator of errors of the second type in EG-
2 was almost twice as high as the results in EG-1 (1.8). It can be concluded that EG-2, which did
not use a termbase with errors, did a better job with the translation. The students relied on
their knowledge, were critical of the text, analyzed its deep meaning, carefully checked and
edited it, and, therefore, received better results. Thus, EG-1 made a significantly greater
number of errors of the first type than EG-2. The total number of errors was also higher in EG-
1.
After a detailed analysis of the translations of the two groups, we conclude that EG-1, which
used the termbase with errors, made a greater number of errors of the first type, while the
errors of the second and third types were not significant in terms of their number concerning
the errors of the first type. EG-2, which did not have a termbase, made a greater number of
errors of the second type, which can be explained by the choice of stylistic synonyms that were
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unsuccessful for the proposed context. Both groups as a whole did not always carefully edit
and proofread the translation.
DISCUSSION
The results of the study partially confirmed the opinion that in the conditions of professional
training of future translators, in some cases the use of CAT tools is not justified since:
CAT systems do not provide acceptable source quality. Higher quality can be achieved
by presetting the system, which is completely unacceptable for small volumes of
translated texts, and/or by subsequent editing, and this only slows down the work if
the translator uses blind typing (CAMPBELL et al., 2013);
CAT systems do not guarantee that terminology is consistent, especially when a team
of translators is working on a large project. Rather, they can be guaranteed, provided
that user dictionaries are treated very carefully, and this is not always worth counting
on (TARAVELLA, VILLENEUVE, 2013).
However, as some methodologists believe (ERWEN, WENMING, 2013), in some cases, the use
of CAT systems helps to reduce time costs. This happens if the text is long and contains uniform
terminology that allows relatively quickly customizing the CAT system for it. Then editing the
text will not take too long. However, in this case, one should be especially careful about the
style of the text. Machine translation is formal; therefore, there is a high probability of tracing
the syntactic structures of the original language, which is typical for translation in general, and,
therefore, it may be missed during editing.
Generally speaking, CAT systems may be used wherever the most standardized language is
used, with simple grammar and relatively small vocabulary. The German program Meteo,
which translates weather forecasts from French into English and vice versa, is considered a
fairly successful project of the CAT system (KOEHN, 2009). To facilitate the work of translators
and technical writers, Boeing once developed a language standard for writing technical
documentation known as Boeing English (LEBERT, 2011).
Currently, the most popular translation memory system in the world is TRADOS, which,
according to Imperial College (London), occupies 35% of the market (WANG, 2012). In
addition to the main module (Workbench), in which the work is performed, it contains several
additional, equally useful modules: MultiTerm a program for creating termbases that
connects to the Workbench, which increases labor productivity; WinAlign a program for
creating TM (Translation Memory) (or blocks of pairs) using existing translations; TagEditor-
program allows working in various formats and performing formatting and many other useful
options (XU, 2010). According to researchers (BOWKER, 2015), in terms of functionality,
WordFast is practically as good as TRADOS but more stable and cheaper.
Speaking about the most promising ways of developing machine translation systems,
researchers (ÇETINER, 2018) propose to focus on creating more efficient electronic
dictionaries with the most efficient search and indexing mechanism and the most integrated
system of dictionary entries. If we consider the development of CAT systems, then the most
promising direction is the improvement of the subsystems of grammatical analysis and
synthesis, as well as an increase in the volume of contextual coverage of the text and
improvement of semantic chains to select meanings of words more accurately.
CONCLUSION
Today, given the rapid development of information technology and steady growth in the
volume of information, translation activities are becoming more and more popular. The
consequence of technical progress is an increase in requirements for the training of future
translators, in particular, for the features of using machine translation, in their professional
training. Therefore, training future translators to use CAT tools and the ability to correctly and
present information obtained from the source is becoming increasingly important and very
relevant.
Zulfiya A. Usmanova; Ekaterina N. Zudilova; Pavel A. Arkatov; Nataliaya G. Vitkovskaya; Ekaterina V. Kravets
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The results of the empirical research carried out confirmed the hypothesis that, firstly, when
translating the OT with a CAT tool using a termbase with erroneously translated lexical units,
the quality of student’s translation will decrease and, secondly, when translating the OT with a
CAT tool without using a termbase with erroneously translated lexical units, the quality of
student translation will improve.
Our experiment proved that when using a termbase, novice translators tend to actively rely on
it without critical analysis of the offered resources. This, in turn, testifies that machine translation
systems can harm the professional activities of novice translators. Therefore, it is important to
have a special organization of training in modern translation technologies, which would
consider the results obtained and have a goal of developing a critical attitude of students to
the mentioned software provision and resources provided. However, due to the small number
of subjects in this experiment, conclusions cannot be final. Therefore, in the future, they should
be tested on a larger sample of students.
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Impact of computer-assisted translation tools by novice translators on the quality of written translations
Impacto das ferramentas de tradução assistidas por computador por tradutores iniciantes sobre a qualidade das
traduções escritas
Impacto de las herramientas de traducción asistida por ordenador por traductores novatos en la calidad de las
traducciones escritas
Resumo
Abstract
Resumen
A principal especificidade do
mercado de tradução moderna é a
tradução de grandes volumes de
textos técnicos e documentos de
negócios no menor tempo possível.
O objetivo do estudo é realizar um
experimento sobre o impacto dos
sistemas de tradução automática (em
termos de uso de bases de termo)
sobre a eficiência dos futuros
tradutores. O estudo fornece uma
revisão de literatura sobre o
problema em estudo e apresenta as
vantagens das ferramentas de
tradução assistidas por computador
na prática de tradução. Com base no
estudo experimental, foi realizada a
análise da influência das ferramentas
de tradução assistidas por
computador na qualidade das
traduções escritas dos tradutores
estudantis.
The main specificity of the modern
translation market is the translation
of large volumes of technical texts
and business documents in the
shortest time possible. The purpose
of the study is to conduct an
experiment on the impact of
machine translation systems (in
terms of using term bases) on the
efficiency of future translators. The
study provides a literature review
on the problem under study and
presents the advantages of
computer-assisted translation tools
in translation practice. Based on the
experimental study, the analysis of
the influence of computer-assisted
translation tools on the quality of
written translations of student
translators was carried out.
La principal especificidad del
mercado de la traducción moderna
es la traducción de grandes
volúmenes de textos técnicos y
documentos comerciales en el
menor tiempo posible. El propósito
del estudio es llevar a cabo un
experimento sobre el impacto de
los sistemas de traducción
automática (en términos de uso de
bases de términos) en la eficiencia
de los futuros traductores. El
estudio proporciona una revisión
de la literatura sobre el problema
en estudio y presenta las ventajas
de las herramientas de traducción
asistida por computadora en la
práctica de la traducción. A partir
del estudio experimental, se realizó
el análisis de la influencia de las
herramientas de traducción asistida
por ordenador en la calidad de las
traducciones escritas de los
estudiantes traductores.
Palavras-chave: Sistemas de
tradução automática. Língua
francesa. Qualidade de tradução.
Keywords: Machine translation
systems. French language.
Translation quality.
Palabras-clave: Sistemas de
traducción automática. Francés.
Calidad de la traducción.
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