TRANSLATION ERRORS, READABILITY, AND ACCEPTABILITY
OF GOOGLE TRANSLATE’S INDONESIAN RENDERINGS
OF TWO CHILDREN’S STORIES
IN LET’S READ MOBILE APPLICATION
AN UNDERGRADUATE THESIS
Presented as Partial Fulfillment of the Requirements
for the Degree of Sarjana Sastra
in English Letters
By
ALMA ANINDITA
Student Number: 194214081
DEPARTMENT OF ENGLISH LETTERS
FACULTY OF LETTERS
UNIVERSITAS SANATA DHARMA
YOGYAKARTA
2022
CHAPTER II
REVIEW OF LITERATURE
This chapter provides further elaboration on the studies that were conducted by
other researchers on similar themes, as well as a discussion of some of the theories that
are utilized in this thesis. Previous studies carrying similar topics in this thesis were taken
from Laempasa, Setiawan, and Matusov’s works. It is necessary to evaluate each study
in detail to identify the similarities and differences to avoid duplication of topics. Some
theories used in this thesis are also evaluated and explored to establish a firm foundation
for this thesis.
A. Review of Related Studies
The first study examined in this research was written by Laempasa (2021). This
undergraduate thesis compares the ability of two machine translation tools, namely
Google Translate (GT) and Bing Translator (BT), in translating a sports news text found
in the New York Times through error analysis. Data in this study consists of words,
phrases, sentences, and paragraphs of the translated news text, which are analyzed based
on Koponen’s error category. The results of the study show that the errors made by GT
are proven to be fewer than the ones made by BT. According to Laempasa, although GT
has superior ability in translating complex sentences when BT fails to do so, other
sentences experienced the opposite, showing that BT is capable of conveying meanings
when GT does not. However, the limitation of Laempasa’s study lies in the fact that she
only analyzed current news text. Therefore, by adding feature news into the evaluation,
the analysis of the machine translation’s performance would become much clearer.
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Laempasa’s research applies a similar error analysis approach to this thesis; both
use Koponen’s theory to categorize the errors found. Moreover, both research evaluate
GT’s ability. In terms of differences, however, Laempasa uses an entirely different object
than this research, namely a news text which was taken from the website
www.nytimes.com. Meanwhile, this research uses two children’s stories taken from the
Let’s Read mobile application. Another difference is that Laempasa’s research only
analyzes the errors found in the Indonesian translation, whereas this research amps up the
analysis by including the readability and acceptability aspects as well.
In his journal article, Setiawan (2020) attempts to analyze whether the translation
of scientific texts is comprehensible, accurate, and reliable. Data in this article were taken
from various international journals which were then classified into two groups. The first
group is designated for Indonesian-English translation, while the second one is for
English-Indonesian translation. Results of this study show that all of the translated texts
are generally comprehensible and acceptable, with minor phrases that were
inappropriately translated due to the context not being considered properly. Particularly,
this research also focuses on several aspects, such as compound words, idiomatic
expressions, and sentence patterns.
Although interesting, some limitations are found in Setiawan’s research. In
particular, it remains unclear what or whose theories the translations are analyzed based
on, especially considering that it aims to discover accuracy, comprehensibility, and
reliability. Furthermore, Setiawan also has not quite elaborated on what kinds of scientific
texts the analysis is conducted on. The author believes that it would be better to set a
specific and distinguishable genre of scientific texts for further analysis to provide a
clearer ground on which subject matter GT’s ability lies.
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In one form, this research possesses a similarity to Setiawan’s, namely that both
analyze the works of GT. However, the objects examined are different; this research
utilizes literary texts while Setiawan uses scientific texts. Another difference would be
that instead of acceptability, his research analyzes the aspect of reliability instead as he
believes that it is more appropriate in the eyes of readers.
The third study examined in this chapter was conducted by Matusov (2019). It
specifically evaluates the neural-machine-translation system to translate literary texts
from German to English and from English to Russian. Results of this study show that up
to 30 percent of machine-translated sentences are of acceptable quality. Even in
complicated phrases, Matusov detected extremely few serious syntactic errors, while
meaning errors for ambiguous terms remain common. Distinct classification of
consistency, pronoun resolution, and tone/register mistake categories provides a
substantial opportunity for MT (machine translation) quality improvements by
considering the context of prior phrases or even the entire text. The evaluation of these
results was conducted manually and compared with the results of a human translator.
Although there are some similarities between the present thesis and this research,
some distinguishable differences are also found. First, both research aims to analyze the
neural-machine-translation quality in translating literary texts; however, Matusov’s
research also incorporates the use AppTek’s NMT (Neural Machine Translation) system
instead of only using GT. Second, the research also uses the presence of human-translated
results as a comparison to the machine-translated ones. This second difference does show
the advantage of his research because it can further clarify the quality of machine
translation.
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B. Review of Related Theories
The second part of this chapter elaborates on the theories used in this thesis.
Overall, seven major theories are utilized as the bases of the research, namely theories of
translation, translation of children’s literature, machine translation, error analysis in
machine translation, Koponen’s error classification, readability, and acceptability. The
theoretical framework, which further describes how the theories mentioned play a role in
this thesis, is provided following this sub-chapter.
1. Theories of translation
According to Munday (2016), the term translation nowadays can refer to several
different meanings, namely the general subject or field of translation, the product of a text
that has been translated, and “the process of producing a translation” (p. 8). He further
elaborates on the last meaning by stating that
the process of translation between two different written languages involves the
changing of an original written text (the source text or ST) in the original verbal
language (the source language or SL) into a written text (the target text or TT) in
a different verbal language (the target language or TL) (p. 8; Munday, 2016).
Munday’s definition appears to be the most common and simplest conception of
what translation activity is, namely to translate a text from one language to another.
The possible way of defining translation certainly does not end there. According
to Nida and Taber, translation can also be referred to as the reproduction of the closest
natural equivalent of the source-language communication in the target language, first
concerning the meaning and then concerning the style (1982, p. 12).
On the other hand, Bell (1991) also argues that translation refers to how
expressions stated in the source language must be transferred into the target language
while maintaining the semantic and stylistic equivalence of the SL (p. 5). Furthermore, in
terms of equivalence accuracy, Bell also argues that such an aspect can be determined by
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examining the semantic features that are shared by both the SL and TL (1991, p. 88). This
latter idea presented by Bell is in line with one of the analyses conducted in this research
which utilizes Koponen’s classification of concept errors based on semantic accuracy.
The three definitions presented above present a thorough theoretical base that this
thesis can rely on. It can be concluded that the act of translation requires the translator to
be fully capable of changing or transferring a text in the source language into the target
language by scrutinizing the equivalence of meaning and style of the TT. Meaning here
is especially related to the semantic features found in both the ST and TT. In this research,
the scope is translation is narrowed down to the analysis of children’s literature, which
according to a number of sources, brings an entirely different set of challenges for
translators.
2. Translation of children’s literature
The rising popularity of children’s literature since the seventies has contributed to
the improvement of the quality and status of books intended for young readers
(Ghesquiere, 2014, p. 24). Amid this development, Ghesquiere further states that the
history of children’s literature shows how translation conduct in relation to it has also
been of great significance by enabling it as a medium to share creativity and both novel
ideas as well as literary models (2014, p. 25).
Certainly, the translation of children’s literature is not an innocent act; in fact, it
poses a different set of challenges and difficulties compared to general literature. Oittinen
starts her elaboration on the ethics of translating children’s literature by stating that
whenever a text is translated, it undeniably involves the take of a new culture and
language as well as new readers and new perspectives (2014, p. 35). Furthermore,
translating texts for children is a reflective action against the conditions of certain cultures
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and languages that translators must adhere to within specific societal and child images
(Oittinen, 2014, p. 35).
Oittinen (2014) further mentions two unique features of translating children’s
literature: its illustrated and read-aloud characteristics as well as dual readership. For
instance, in many cases, children’s literature encompasses the special use of figurative
language such as rhyme. When read out loud, the rhyming becomes an integral
component of its appeal and quality; if omitted or changed in a way that does not play in
favor of the story, its quality may decrease. It is illustrative in nature; in other words, it
requires strong expressions that translators must also capture in addition to maintaining
the unique language style.
The aspect of dual readership is another characteristic that translators must be
aware of. According to Oittinen, in some cases, a book that was initially meant for adults
can instead be a story appropriately read by children, or vice versa (2014, p. 35). This
aspect can be seen in the case of Salman Rushdie’s “Haroun and the Sea of Stories” as
elaborated by Rudvin & Orlati (2014). The book itself tells the story of Haroun, the main
character, and his journey of maturing and growing in the wake of a catastrophe, as he
overcomes a series of challenges and obstacles. “Haroun and the Sea of Stories” manages
to cater to both adults and children by presenting multiple layers of language and content
complexity. Citing Hunt (1991), Rudvin & Orlati note and specify several features often
found in children’s literature,
… he provides us with a useful list of typical (perhaps stereotypical)
characteristics of the language and style of children's literature which, for what
concerns language and structure, could be summarized as follows: childorientedness, simplicity, easy structure, a narrow range of grammatical and
lexical patterns, simple lexis and register, standard set phrases, words from
everyday life, repetitions, short texts and sentences (see Hunt 1991:62) (2014,
p. 162; emphasis added).
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In the case of “Haroun and the Sea of Stories”, they further state that the language
used in the story is in accordance with Hunt’s description as it is shown to employ a
childlike variety of vocabularies and grammatical structures, particularly in the voices of
the narrator and the main protagonist (2014, p. 162). In addition, it manages to incorporate
elements that most of the time only adults can truly comprehend through Rushdie’s use
of “irony, allusion, metaphor, intertextuality and 'hidden' adult subtexts” (Rudvin & Orlati,
2014, p. 163) and how the plot “embeds the political/social message into a frame that is
itself a message” (p. 167).
Any translators are then challenged when faced with the complexity of dual
readership. As Metcalf (2003) states
more children’s books than ever before address a dual audience of children and
adults, which on the other hand comes with a dual challenge for the translator,
who now has to address both audiences in the translated literature” (p. 323).
Therefore, they must be able to preserve the multilayered nature of the text which
inherently consists of two: one that is easy enough for a child and another that is complex
enough for adults (Alla, 2015, p. 16).
It is clear that the characteristics of children’s literature trigger the complexity of
producing its related translation. On one hand, human translators have the advantage of
handling literary texts, specifically those of children’s literature, as they possess the
cognitive capacity to read beyond the written words and consider the aforementioned
aspects. Thus, their ability is superior though they may still experience difficulties.
Machine translation (MT), on the other, is an artificial intelligence (AI) working solely
based on its computerized systems. However sophisticated it may be, the researcher
believes that its ability in translating literary texts is still questioned. Further elaboration
on theories regarding MT and its capacity is provided in the subsequent point.
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3. Machine translation
The existence of a fully automated way of translating text has long been a hope
and goal. This has become possible due to the role of machine translation, which can be
generally defined as computerized systems that are tasked with the production of
translations from one natural language into another, either with or without the assistance
of a human translator (Hutchins & Somers, 1992, p. 3). In terms of difficulties, however,
machine translation tools have their challenges. In particular, one of its biggest hurdles is
the quality of its translation product, which Rivera-Trigueros (2021) believe to be subpar
to ones generated by a human translator, especially those of professional expertise (p.
594). The disadvantages of machine translation, or in this thesis will be specified as GT,
are intriguing to be further looked into as GT’s significant development over the years
has been aimed to improve the “brain” of the machine.
When GT had its phrase-based, statistical machine revolutionized and
transformed into a neural one, the company CEO, Sundar Pichai, revealed how its
algorithm would now be able to replicate the way neurons work in a human brain (King,
2019, p. 2). Despite this development, GT’s NMT system is still found to perform a
number of inadequacies. King (2019) reports that there are weaknesses in the NMT
program, namely “(1) the time and “computational resources” needed to create an
effective tool, (2) lack of robustness in translating “rare” words, and (3) a tendency to
leave out words in the translation process” (p. 10). She elaborates the first point further,
referring to computation resources as the set of data or corpora utilized to train the
algorithm of the machine itself (p. 10). In addition to requiring large data sets, this part is
also time-consuming and, referencing the research conducted by Yonghui Wu et al., is
also expensive (King, 2019, p. 10). In the case of literary translation, King also states that
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based on the experiments she conducted, as well as from other literary scholars and
translators, GT even with its state-of-the-art NMT system is still insufficiently capable of
producing acceptable results and acts as “stand-alone translation tool” when used for
complex texts such as poetry and prose (2019, p. 8). Furthermore, this is in line with
Hutchins’s (2003) assertion that machine translation is generally still quite prone to
features commonly found in literature, for instance, ambiguous vocabulary, complex
syntactic structure, and complicated grammatical constructions (pp. 2-3).
It is clear that although GT is a well-developed machine with constant
improvements made to its AI, its struggle to keep up with technological and language
hurdles remains challenging. In this thesis, the researcher believes that one of the most
common flaws of MT is creating errors in its product. Thus, in the case of translating
children’s literature, the errors produced by MT are scrutinized using a specific approach
called error analysis.
4. Error analysis in machine translation
The previous theoretical description has presented the very basics of machine
translation. However, due to its automated nature, translation errors are bound to occur.
This phenomenon then becomes an opportunity for linguists and other experts to analyze
translation errors and the explanation behind them. According to Stymne and Ahrenberg
(2012), error analysis refers to the process of identifying and categorizing specific errors
in a text that has been translated via machine-assisted tools (p. 1785). As a result, this
kind of analysis can point out particular aspects of a machine translation system's
strengths as well as its weaknesses. Furthermore, by conducting the identification and
classification of translation errors, researchers would be able to establish an error profile
for a translation output and create better decisions concerning the development of a
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machine translation system (Popovic, 2018, pp. 130-131). In assessing MT’s ability, this
research utilizes the classification of errors proposed by Koponen; an elaboration of this
component is provided below.
5. Koponen’s error classification in evaluating machine translation
Analyzing the errors found in machine translation products has the potential of
becoming a valuable opportunity to develop related MTs. To specify the analysis
conducted in this study, the researcher applies Koponen’s error classification to help
identify and classify the kinds of errors found in the selected research object. Thus, the
quality of the machine-translated results of this thesis’ object can be determined.
In regards to what constitutes the quality evaluation of the machine translation
system, Koponen states in her journal that semantic content accuracy is vital, particularly
concerning the use of the MT system for information purposes (2010, p. 1). In terms of
semantic accuracy, Koponen refers to translation error as an occurrence where the source
and target texts do not share the same semantic components. She then further defines
semantic components as “individual concepts and the semantic relations between two
concepts (head and dependent)” (2010, p. 3). This definition also classifies translation
errors in terms of two aspects, namely Concepts and Relations.
Errors related to concepts are divided into six types: added, omitted,
mistranslated, untranslated, explicitated, and substituted (Koponen, 2010, p. 4). The
aspect of Concepts is represented by content words that are potentially larger than
individual words, such as compound nouns and idioms. To provide further examples, the
researcher has collected the following examples from Ariany’s (2017) descriptions in her
study, with some minor changes to avoid blatant plagiarism.
Table 1. Koponen’s Types of Concept Errors
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Types of
Concept
Errors
Meaning
Added
A TT concept is not present in the ST.
Omitted
An ST concept is not conveyed by the TT.
Mistranslated
A TT concept has the wrong meaning for the
context.
Untranslated
SL words appear in the TT.
Explicitated
Substituted
A TT concept explicitly states information left
implicit in the ST without adding information.
The TT concept is not a direct lexical
equivalent of the ST concept but can be
considered a valid replacement for the
content.
Example
ST: The motor cars were gone.
TT: Motor mobil pergi.
ST: I’m going up and fetching that birdy.
TT: Aku akan mendapatkan burung itu.
ST: They were small palms.
TT: Ada telapak tangan kecil.
ST: I’m cooking bacon for dinner.
TT: Aku memasak bacon untuk makan
malam.
ST: They were tall palms.
TT: Ada pohon tinggi.
ST: Her husband looked out of the door.
TT: Suaminya menjenguk dari pintu.
Koponen’s relation errors are classified into eight types, namely mistaken
participant, mistaken relation, substituted participant, substituted relation, added
participant, added relation, omitted participant, and omitted relation. These types of
errors are conveyed via the use of inflection, word order, and function words (2010, p. 3).
Moreover, the examples in this explanation were obtained from Laempasa’s (2021) thesis,
with some minor changes to avoid blatant plagiarism.
Table 2. Koponen’s Types of Relation Errors
Types of
Relation
Errors
Mistaken
Participant
(MP)
Mistaken
Relation (MR)
Substituted
Participant
(SP)
Substituted
Relation (SR)
Meaning
Example
The head or dependent of the relation is
different in the ST and TT; the entity is not
the same.
ST: I found an old book.
TT: Aku menemukan tua buku.
The relation between the two concepts is
different in the ST and TT; the role is
changed.
ST: He hadn’t looked away from him since
he started to speak
TT: Dia tidak melihat darinya karena dia
mulai berbicara
The head or dependent of the relation is
different in the ST and TT; the entity is the
same.
The relation between the two concepts is
different in the ST and TT; the semantic roles
are the same.
ST: citizens who are national of ...
TT: warga negara dari ...
ST: The treaty of Lisbon amends ...
TT: with the treaty of Lisbon is amended.
Added
Participant
(AP)
TT relation not being present in ST introduces
an added concept.
ST: in such a way that …
TT: dalam sedemikian rupa sehingga …
Added
Relation (AR)
TT relation not being present in the ST is
caused by morpho-syntactic errors
ST: The padrone made him very small
TT: Padrone yang membuatnya merasa
sangat kecil
ST relation is not conveyed by the TT due to
an omitted head or dependent.
ST: Her husband was looking out of the door
TT: Suaminya sedang keluar dari pintu
ST relation is not conveyed by the TT due to
morpho-syntactic errors that prevent parsing
ST: …lying propped up with the two pillows
at the foot in the couch
Omitted
Participant
(OP)
Omitted
Relation (OR):
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Types of
Relation
Errors
Meaning
Example
the relation although both concepts are
present in TT.
TT: … berbaring didukung dengan bantal dua
kaki di sofa
Although both theories are significantly useful for the analysis conducted in this
present thesis, the researcher only exclusively applies Koponen’s theory of individual
concept errors to narrow down the analysis. This choice is made on the account that the
researcher is only focused on the content or concepts and not on the relation. Evaluation
of the MT’s ability does not end in mere error analysis as the aspect of readability is also
an integral part of this research.
6. Readability
Readability is another aspect of translation quality assessment. According to
McDonald, readability refers to how well a text can be read and understood by the readers
(2020, p. 5). This is in line with Nababan’s assertion that in the context of translation,
readability is not only focused on the source text but the target text as well (2012, p. 45).
Translation can be considered to have a high readability score when it can be read with
ease and the message is comprehensible even when it does not necessarily match the
message conveyed in the source text. Furthermore, the role of the readers in determining
the readability of a text is prominent (McDonald, 2020, p. 5).
In assessing the translation readability, Nababan’s rubric becomes the main
assessment tool. Originally written in Indonesian, the rubric is translated into English,
and both are presented below.
Table 3. Nababan’s Translation Readability Rubric (Original)
Kategori Terjemahan
Tingkat Keterbacaan Tinggi
Skor
3
Parameter Kualitatif
Kata, istilah teknis, frasa, klausa, kalimat atau teks terjemahan
dapat dipahami dengan mudah oleh pembaca.
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Kategori Terjemahan
Skor
Parameter Kualitatif
Tingkat Keterbacaan Sedang
2
Pada umumnya terjemahan dapat dipahami oleh pembaca; namun
ada bagian tertentu yang harus dibaca lebih dari satu kali untuk
memahami terjemahan.
Tingkat Keterbacaan Rendah
1
Terjemahan sulit dipahami oleh pembaca
As seen above, Nababan’s rubric measures the readability level on a score from 1
to 3, lowest to highest, respectively. Each score mirrors a parameter that measures a text’s
readability based on how easy or difficult it is to understand the words, phrases, clauses,
sentences, certain terminologies, etc. Subsequent to the original rubric, the following
English version is translated by the researcher herself.
Table 4. Nababan’s Translation Readability Rubric (Translated)
Translation Category
Score
High Readability Level
3
Medium Readability Level
2
Low Readability Level
1
Qualitative Parameters
Words, technical terms, phrases, clauses, sentences or translated
text can be easily understood by the readers.
In general, the translation can be understood by the readers, but
certain parts must be read more than once.
The translation is difficult for the readers to understand.
The aspect of readability itself has the lowest score out of the three aspects in
Nababan’s translation quality assessment model, namely the score of 1, compared to
accuracy (score of 3) and acceptability (score of 2) (2012, p. 52). The low score given to
readability stems from the idea that the issue of translation is not directly tied to whether
or not the translation is easy to understand for the target readers. However, as most of
them do not have access to the source text, they have hopes that the translations they read
are easily understandable (2012, p. 52).
Table 5. Translation Readability Range
Readability Category
Range
High Readability
3-2.6
Medium Readability
2.5-2.1
Low Readability
2-1.9
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Table 5 above shows the readability score range employed in this study, with
which the researcher can classify which datum is put in which category. The range itself
is acquired using normal curve, and the calculation is provided below.
𝑯𝒊 𝒔𝒄𝒐𝒓𝒆−𝑳𝒐 𝒔𝒄𝒐𝒓𝒆
X = 𝑵𝒐.𝒐𝒇
𝒓𝒂𝒏𝒈𝒆 𝒄𝒂𝒕𝒆𝒈𝒐𝒓𝒊𝒆𝒔
To find the range, a starting step is to subtract the highest average score from the lowest
average score. Both of these were obtained from the respondents after filling out the
readability questionnaire. Subsequently, it is divided by the number of range categories,
which in this case, there are three.
X = 𝟑−𝟏.𝟗
= 0.366
𝟑
Once the x is found, each range for the three categories can now be calculated. In the
following formulas, R1 refers to range 1 (high readability), R2 refers to range 2 (medium
readability), and R3 refers to range 3 (low readability).
R1 = Hi score – X
R1 = 3 – 0.366 = 2.6
In R1, it can be seen that the score range for the high readability category falls between
2.6 – 3 (the highest score).
R2 = (R1 – 0.1) – 0.366
R2 = (2.6 – 0.1) – 0.366
R2 = 2.5 – 0.366 = 2.1
Meanwhile, in R2, the score range for the medium readability category falls between 2.1
– 2.5.
R3 = R2 – 0.1
R3 = 2.1 – 0.1
R3 = 2.0
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Lastly, the score range for the low readability category falls between 1.9 (the lowest score)
– 2.0. In addition to readability, acceptability analysis is also applied in this research to
add further clarity to the evaluation of MT.
7. Acceptability
The term translation acceptability, according to Nababan et al. (2012), refers to
how a translated work can comply with the norms, rules, and cultures of the target text,
both at micro and macro levels (pp. 44-45). Furthermore, the relativity of this concept
also depends on the cultural norms of a particular society (2012, p. 45). For instance,
expressions considered to be polite and common by Americans may not be the same as
what is deemed polite and common in Indonesia. Therefore, acceptability puts forward
the view that a translation is considered adequate if it follows the norms coming from the
culture of the source, while the translation is deemed acceptable if it follows the norms
coming from the culture of the target (McDonald, 2020, p. 5).
Assessing the translation acceptability utilizes Nababan’s rubric as the tool.
Similar to the readability one, the rubric is originally written in Indonesian and in this
thesis, is translated into English.
Table 6. Nababan’s Translation Acceptability Rubric (Original)
Kategori Terjemahan
Score
Berterima
3
Kurang Berterima
2
Tidak Berterima
1
Qualitative Parameters
Terjemahan terasa alamiah; istilah teknis yang digunakan lazim
digunakan dan akrab bagi pembaca; frasa, klausa dan kalimat yang
digunakan sudah sesuai dengan kaidah-kaidah bahasa Indonesia
Pada umumnya terjemahan sudah terasa alamiah; namun ada
sedikit masalah pada penggunaan istilah teknis atau terjadi sedikit
kesalahan gramatikal.
Terjemahan tidak alamiah atau terasa seperti karya terjemahan;
istilah teknis yang digunakan tidak lazim digunakan dan tidak
akrab bagi pembaca; frasa, klausa dan kalimat yang digunakan
tidak sesuai dengan kaidah-kaidah bahasa Indonesia
Similar to the readability rubric, the acceptability indicator measures a text’s
acceptability based on a scoring system ranging from 1 to 3, with 3 being the greatest
possible score. According to this rubric, whether or not a text is acceptable – or less
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acceptable – is assessed based on the familiarity or unfamiliarity of the terms used in the
text. Moreover, it also scrutinizes the compliance of the target text with the rules and
conventions of the target language, grammatical errors, and how the translation feels in
the hearts of the readers.
Subsequent to the original rubric, the following English version is translated by
the researcher herself.
Table 7. Nababan’s Translation Acceptability Rubric (Translated)
Translation Category
Score
Acceptable
3
Less Acceptable
2
Unacceptable
1
Qualitative Parameters
Translation feels natural; technical terms used are indeed common
to use, and the readers are familiar with them; phrases, clauses, and
sentences used are in accordance with Indonesian rules and
conventions.
In general, translation feels natural; however, there are slight
issues with the use of technical terms or there are slight
grammatical errors.
Translation does not feel natural or feel like it is a translation;
technical terms used are uncommon to use, and readers are not
familiar with them; phrases, clauses, and sentences used are not in
accordance with Indonesian rules and conventions.
As mentioned previously, the aspect of acceptability is placed second on
Nababan’s QTA model; this is founded on the idea that the acceptability of a translation
is directly tied to its conformity with the applicable rules, norms, and culture of the target
language. In some instances, the acceptability component impacts the accuracy aspect, in
which a less acceptable or unacceptable translation will also potentially be less accurate
or inaccurate (2012, p. 52).
Table 8. Translation Acceptability Range
Acceptability Category
Range
Acceptable
2.9-2.4
Less Acceptable
2.3-1.8
Unacceptable
1.7-1.5
In table 8, ranges for the acceptability categories are provided. Similar to the
readability range, the researcher utilizes this range to classify each datum according to
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the average score it received. Calculation of the acceptability range using normal curve
is presented below.
𝑯𝒊 𝒔𝒄𝒐𝒓𝒆−𝑳𝒐 𝒔𝒄𝒐𝒓𝒆
X = 𝑵𝒐.𝒐𝒇
𝒓𝒂𝒏𝒈𝒆 𝒄𝒂𝒕𝒆𝒈𝒐𝒓𝒊𝒆𝒔
To find the range, a starting step is to subtract the highest average score from the lowest
average score. Both of these were obtained from the respondents after filling out the
acceptability questionnaire. Subsequently, it is divided by the number of range categories,
which in this case, there are three.
X = 𝟐.𝟗−𝟏.𝟓
= 0.466
𝟑
Once the x is found, each range for the three categories can now be calculated. In the
following formulas, R1 refers to range 1 (acceptable), R2 refers to range 2 (less
acceptable), and R3 refers to range 3 (unacceptable).
R1 = Hi score – X
R1 = 2.9 – 0.466 = 2.4
In R1, it can be seen that the score range for acceptable translation falls between 2.4 – 2.9
(the highest score).
R2 = (R1 – 0.1) – 0.466
R2 = (2.4 – 0.1) – 0.466
R2 = 2.3 – 0.466 = 1.8
Meanwhile, in R2, the score range for less acceptable translation falls between 1.8 – 2.3.
R3 = R2 – 0.1
R3 = 1.8 – 0.1
R3 = 1.7
Lastly, the score range for unacceptable translation falls between 1.5 (the lowest score) –
1.7
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All principal theories used in this research have been elaborated. Subsequently,
the theoretical framework of this thesis presents the cohesion between the theories and
the research questions. In other words, it lays out the basic mapping of how the theories
are used to acquire the results.
C. Theoretical Framework
The definitions of translation ascertained by Munday as well as Nida and Taber
are utilized as the fundamental stepping stone in discussing the topic of this study.
Following this elaboration, the theory of children’s literature translation is also applied as
it becomes another foundation of how this specific type of literature must be treated
differently. Once the basics of translation and one regarding children’s literature are laid
out, the discussion of machine translation, as well as its advantages and challenges, can
be established to first see what issues may arise in evaluating the ability of GT’s NMT.
The main points of this study are then analyzed per the order of the theories: errors are
analyzed based on Koponen’s individual concept errors; subsequently, the readability and
acceptability of the translation results are observed and assessed. Related methodologies
for all analyses are elaborated further in the following chapte
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