ERROR ANALYSIS IN MACHINE TRANSLATION OF CRISTIANO RONALDO INSTAGRAM CAPTION
THESIS
Submitted in Partial Fulfillment of the Requirements for the Degree of Sarjana Humaniora
Written by:
Joko Trimanto SRN. 173211117
ENGLISH LETTERS STUDY PROGRAM FACULTY OF CULTURES AND LANGUAGES
UIN RADEN MAS SAID SURAKARTA 2022
ADVISOR SHEET
Subject : Thesis of Joko Trimanto SRN : 173211117
To:
The Dean of
Faculty of Cultures and Languages
UIN Raden Mas Said Surakarta In Surakarta
Assalamu’alaikum warahmatullahi wabarakatuh
After reading thoroughly and giving necessary advices, herewith, as the advisor, I state that the thesis of
Name : Joko Trimanto SRN : 173211117
Title : Error Analysis In Machine Translation Of Cristiano Ronaldo
Instagram Caption
has already fulfilled the requirements to be presented before The Board of Examiners (Munaqosyah) to gain the Degree of Sarjana Humaniora in English Letters.
Thank you for the attention.
Wassalamu’alaikum warahmatullahi wabarakatuh
Surakarta, 22st of September, 2022 Advisor,
Dr.Hj. Lilik Untari, S.Pd., M.Hum.
NIP. 19751005 199803 2 002
RATIFICATION
This is to certify that the Sarjana thesis entitled Error Analysis In Machine Translation Of Cristiano Ronaldo Instagram Caption by Joko Trimanto has been approved by the Board of Thesis Examiners as the requirement for the degree of Sarjana Humaniora in English Letters.
Main Examiner : Dr. Luthfie Arguby Purnomo
(...) NIP. 19820906 200604 1 006
Chairman : Robith Khoiril Umam, S.S., M.Hum.
(...)
NIP. 19871011 201503 1 006 Secretary : Dr.Hj. Lilik Untari, S.Pd., M.Hum.
NIP. 19751005 199803 2 002 (...)
Surakarta, 22th of September, 2022 Approved by
The Dean of Faculty of Cultures and Languages
Prof. Dr. H. Toto Suharto, S.Ag., M.Ag.
NIP. 19710403 199803 1 005
DEDICATION
This thesis is dedicated to:
1. My Self 2. My Family
3. All of My Teachers in This Universe 4. My Almamater
5. For The Readers
MOTTO
Just Do It!
(Nike Football)
Matikan Handphonemu, Hidupkan Skripsimu (Joko.Trie)
PRONOUNCEMENT
Name : Joko Trimanto
SRN : 173211117
Study Program : English Letters
Faculty : Faculty of Cultures and Languages
I hereby sincerely state that the thesis entitled Error Analysis In Machine Translation Of Cristiano Ronaldo Instagram Caption is my own original work.
To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due references are made.
If later proven that my thesis has discrepancies, I am willing to take the academic sanctions in the form of repealing my thesis and academic degree.
Surakarta, 22th of September, 2022 Stated by,
Joko Trimanto SRN. 173211117
ACKNOWLEDGEMENT
Alhamdulillah, all praises be to Allah Swt, the Almighty, the Lord of the Universe for all the blessings and graces, so that the researcher was able to complete the undergraduate thesis entitled Error Analysis In Machine Translation Of Cristiano Ronaldo Instagram Caption. Peace be upon Prophet Muhammad saw, who has led us from the darkness to the lightness.
The researcher believes this thesis will not be complied without help, support, and suggestions of various parties. For that, the researcher would like to thank all those who have helped, supported, and suggested it during the process of this thesis. This goes to:
1. Dr. Hj. Lilik Untari, S.Pd., M.Hum as the researcher advisor, and examiner, for his best advices, guidance, corrections, sincere, and motivations for the researcher. May Allah Swt gives you and your family health and bless.
2. Dr. Luthfie Arguby Purnomo as the main examiner in this research, for the guidance and advices for the researcher.
3. Robith Khoiril Umam, S.S., M.Hum., as the second examiner in this research, for the guidance and advices for the researcher.
4. Arkin Haris, S.Pd., M.Hum., as the validator of the data who checks the correctness of the data and its analysis.
5. SF. Lukfianka Sanjaya Purnama, S.S., M.Hum., as the academic advisor
6. All of the lectures of English Letters Department, for the all knowledge, motivation, kindness, exhilaration, and advices for the researcher.
7. For English Letters D class (Deletrius), who always be the good friends to share and help each other.
8. For the researcher kind-hearted friends, who always give support, advices, love and, their time to listen to restlessness and cheer up.
The researcher realizes that this thesis is far from being perfect. Thus, any suggestions are received for the improvement of this research. Hopefully, this research can give positive impacts to the readers as well as those want to carry out further research.
Surakarta, 22st of September, 2022 Stated by
Joko Trimanto SRN. 173211117
TABLE OF CONTENTS
THESIS ... i
RATIFICATION ... iii
DEDICATION ... iv
MOTTO ... v
PRONOUNCEMENT ... vi
TABLE OF CONTENTS ... ix
ABSTRACT ... xii
LIST OF TABLES ... xiii
LIST OF FIGURES ... xiv
LIST OF ABBREVIATIONS ... xv
CHAPTER I INTRODUCTION ... 1
A. Background of the Study ... 1
B. Limitation of the Study ... 6
C. Formulation of the Problem ... 7
D. Objective of the Study ... 7
E. Benefits of the Study ... 7
F. Definition of the Key Terms ... 8
1. Translation ... 8
2. Machine translation ... 9
3. Instagram ... 9
4. Caption ... 9
5. Error ... 9
CHAPTER II LITERATURE REVIEW ... 11
A. Theoritical Background ... 11
1. Translation ... 11
2. Process of Translation ... 12
3. Tanslation Strategies ... 14
a. Borrowing ... 15
b. Calque ... 15
c. Literal translation ... 16
d. Transposition ... 16
e. Modulation ... 17
f. Equivalence ... 17
g. Adaptation ... 18
4. Error Analysis in Machine Translation ... 18
5. Errors Classification in Machine Translation ... 19
a. Omitted Concept ... 20
b. Added Concept ... 20
c. Mistranslated Concept ... 20
d. Untranslated Concept ... 21
e. Substituted Concept ... 21
f. Explicitated Concept ... 21
B. Previous Related Studies ... 22
CHAPTER III RESEARCH METHOD ... 25
A. Research Design ... 25
B. Data and Source of Data ... 26
C. Research Instruments ... 27
D. Data Collection Techniques ... 28
E. Technique of Analyzing Data ... 30
1. Domain analysis ... 31
2. Taxonomy analysis ... 31
3. Componential analysis ... 32
4. Discovering cultural theme ... 34
F. Data Validation Techniques ... 34
CHAPTER IV FINDING AND DISCUSSION... 36
A. Research Findings ... 36
1. The translation strategies empoyed in Cristiano Ronaldo Instagram caption ... 37
a) Borrowing ... 38
b) Calque ... 40
c) Literal translation ... 42
d) Transposition ... 46
e) Modulation ... 49
f) Equivalence ... 51
g) Adaptation strategy ... 51
2. Machine Translation Error Types ... 54
a. Omitted Concept ... 55
b. Added concept ... 58
c. Mistranslated Concept ... 60
d. Untranslated Concept ... 62
e. Substituted Concept ... 64
f. Explicitated Concept ... 64
B. Discussion ... 67
CHAPTER V CONCLUSION ... 72
A. Conclusions... 72
B. Implications ... 74
C. Suggestions ... 74
BIBLIOGRAPHY ... 75
ABSTRACT
Joko Trimanto. (2022). Error Analysis In Machine Translation Of Cristiano Ronaldo Instagram Caption. English Letters. Faculty of Cultures and Languages.
Advisor : Dr. Hj. Lilik Untari, S.Pd., M.Hum
Keyword : Machine Translation, Translation Strategies, Machine Translation error analys
This research analyzes macine translation proces in Cristiano Ronaldo Isagram cations. Its use has become a phenomenon that is often found in machine transation product, this research intends to analyze the translation strategies and machine translation error analys. The first purpose of this research is to find out the translation startegies are product by Instagram machine translation in Cristian Ronaldo Instagram account. The second is to describe the machine translation error types in in Cristiano Ronaldo Instagram caption.
This research is descriptive qualitative research. The data of this research are found in machine translation product on Instagram captions of Cristiano Ronaldo Instagram account. The data are collected by documentation technique.
This study applies two theories. The first is seven translation strategies proposed by Vinay and Darbelnet (2000). The second is Assessing Machine Translation Quality with Error Analysis Koponen (2010).
The research findings revealed two points of the problems in this research. The first, this research translation techniques that found on Cristiano Ronaldo Instagram account. There are borrowing strategies with 5 data, calque strategies with 17 data, literal translation with 46 data, Transposition with 7 data, modulation with 2 data, and adaptation with 3 data. The second, this research machine translation error, The categories include omitted concept with 24 data, added concept with 3 data, mistranslated concept with 37 data, untranslated concept with 12 data, substituted concept with 0 data, and explicitated concept with 4 data.
The result of this research, the most dominant data found of this research is literal translation from translation stategies with 46 and mistranslated concept with 37 data. It happens because the machine translator on Instagram is still not too perfect to translate our language from every word so that Miss Translate is still frequent. but for the machine translator on Instagram it is also enough for Instagram users to understand according to the language they know,
LIST OF TABLES Table 3.1 The Taxonomy Data
Table 3.2 Componential Table Analysis Tabel. 4. 1 Translation Strategies Finding Tabel 4. 2 Machine Translation Error Types
LIST OF FIGURES
Figure 2.1 : Machine Translation Process
LIST OF ABBREVIATIONS ST : Source Text
TT : Target Text
OC : Omitted Concept AC : Added Concept
MC : Mistranslated Concept UC : Untranslated Concept SC : Substituted Concept EC : Explicitated Concept B : Borrowing
C : Calque
LT : Literal translation M : Modulation E : Equivalence A : Adaption T : Transposition Sw : Same word
Wt : Without Translation Ex : expression
Gr : Grammatically Wc : Word Class Mf : Message form Ph : Phrase
CHAPTER I INTRODUCTION
A. Background of the Study
Technology has progressed since the development of the first computer.
The amount of research done on using a computer as a machine that can translate one language into another has increased significantly. Although machine translation (MT) had a tentative start in the 1950s, it has continued with the advent of numerous MT systems in the 1980s. Then, throughout the 2000s, it continued to evolve into more diverse and bigger systems. As part of the computers' agenda, Hutchins W J, Somers H L (2003) claims that MT has evolved into a large-scale, commercial system.
As a large-scale computer system, MT provides a great deal of pragmatism, simplicity, and efficiency, to the point that it has become a shortcut for many individuals to overcome their language problem in communication.
MT allows for a translation process that involves little or no human input (Roberts, 2002). In the process of translating one language into another, MT follows its pattern on an automatic system and database. Users only need to enter a text into the MT, and it will handle the rest. As a result, MT makes the translation process simple and produces a quick result.
Language is one of the most significant aspects of human social relationships in this period. It is the ability of humans to use advanced systems of communication, and language is one example of such a system. There are different languages spoken throughout the world, including Indonesian, German,
Russian, Spanish, African, and others. English is spoken as a first or second language by people all over the world. One of the most popular language is English, which is known as a global language. English is used in several sectors of world life, such as poems in films, music lyrics, novels, etc.
Internet is growing rapidly, now SIM card, modem, or Wi-Fi, and a smartphone or computer, is required to access the internet and be connect to others in different countries around the world. The internet provides access to a lot of information, knowledge, social media, and other resources. In translation, it is not enough to just translate the original text; it is also important to understand the culture, customs, and socialization of the speakers of the source and target languages, amongst many other aspects.
It is very important to study about translation in English because translation also plays a very important role in communication between two different languages can understand one another easily. Larson (1984) states that translation consists of studying the lexicon, grammatical structure, communication situation, and cultural context of the SL text, analyzing it so as to work out its meaning, then reconstructing the identical meaning, and then reconstruct the same meaning using lexical and grammatical structures relevant to the target languages and their cultural context. Larson also says that translation has three steps they're studying the source text, analyzing it and reconstructing the meaning. The process of automatically transforming source material in one language to text in another language is known as machine translation. In a machine translation task, the input consists of a sequence of
symbols in some language, which the computer software must turn to a sequence of symbols.
Machine translation offers a great deal of practicality, simplicity, and efficiency, to the extent that it has become a shortcut for many people in the communication process. The use of machine translation (MT) enables for a translation process with little or no human intervention (Roberts, 2002), In the process of translating one language into another, MT follows a pattern that is dependent on an automatic system and database. MT users need only enter a text into the MT, and it will perform the rest. As a result, MT makes the process simple and gives a quick translation product. Instagram application is also provided with Instagram Machine Translation; if Instagram users want to change the translation of the caption (text) on Instagram, they only need to click the button “See translation” that is available on Instagram application.
Social media is that the place of the many various sorts of translation.
There is such a large amount of social media application, Facebook, Twitter, Snapchat, Instagram and etc. And one amongst popular social media application is Instagram. There is such a large amount of people within the world and from so many countries. Users can add a caption in his post and might watch to every of their followers and use hash-tags and site based and make them post view by one another user. As with other social networking platforms, Instagram users can like and also the like may be a love, treat and send a private message, call, voice note and video call to their friends via the Instagram Direct messages.
Sometime Instagram post with other language so can not understand the meaning of the caption, but in Instagram have a tools features machine
translation, people to understand the caption within the Instagram caption even they used different language.
Translation product implemented into many social media platforms, such as Instagram, can help users who speak different languages. Instagram is a social media platform that was created in 2010. It is a free photo and video sharing app that is available for Android, Apple iOS, and Windows Phone. This app can be used on any smartphone with an internet connection. Instagram introduced a translation feature in 2016. The machine translation tool can translate the language within the caption which used different language into the language which is installed in the cellphone. For instance, the phone was installed in Indonesian language as default language. Then look at bottom of caption and click the word translate interpretation tool set to translate from any different language into Indonesian. the interpretation tool because the computational linguistics in Instagram is ready to translate not only in caption, but also in comment are able to translate Machine Translation tool which is provided by Instagram sometimes can help user to grasp the difference of language, but sometimes translation tool product the incorrect language which is unread ability and find some error.
In this research, the researcher try to observe and analyze the errors made in translation process of made by MT Instagram captions. Instagram MT is statistical machine translation. This research aimed to analyze of error in Instagram Machine Translation caption from English to Indonesian. The type of error and strategies that happened in this thesis were the focus of the investigation. The researcher then wishes to explain translation error product
about the language used in Instagram Machine Translation captions. Sometimes translation errors occur when sentences combine to form a long word that the translator's computer is unable to process correctly. In order to translate the source target (ST), the translator must analyze how the text's equivalence to the correct target text (TT) can be achieved.
Error Analysis in Machine Translation is “The identification and classification of individual errors in a machine translated text," according to Error Analysis in Machine Translation. It is also a way to get a qualitative look at machine translation output, which can be used to generate error profiles for various systems” (Stymne and Ahrenberg, 2012), Instagram machine translation works only need to click the button "See translation" that is available on Instagram application, and Machine translation of Instagram caption error will know that it shows error can make the translate is readable but it slow is make a mis-understanding. For Example:
ST: It wasn’t the result we wanted but Head up and keep spirit TT: Itu bukan hasil yang kami inginkan, tetapi Tetap semangat dan tetap berjiwa.
From the example above, there is an Misstranslated concept that can make researchers and Instagram users feel that there are sentences that are not standardized and can make misunderstandings, correctly the word 'spirit' which is translated into 'berjiwa' the correct translation should be 'bersemangat'. The reason for researching the machine translation error type because the translation product of Instagram Machine Translation, the researcher provides an explanation of machine translation errors and makes Instagram users easier to
understand. And the reason for selecting the Machine Translation error analysis of Cristiano Ronaldo Instagram caption is to research the caption of machine translation translate the text and why the researcher select Cristiano Ronaldo Instagram caption is because Cristiano Ronaldo may be a famous football players in the word who had a biggest followers on Instagram and is additionally a supporter of individuals with disabilities and may be a one that loves nature and animals, and in every caption its meaning is exact which makes its followers excited and might motivate the reader. Based on the reason above, the researcher said that he would do the research entitled ERROR ANALYSIS IN MACHINE TRANSLATION OF CRISTIANO RONALDO INSTAGRAM CAPTION.
B. Limitation of the Study
To limit the problem in this research, the researcher made limitation for both the object and the variable. This research is to analyze the error found from Instagram Machine Translation caption from Instagram Cristiano Ronaldo researchers chose posts with a football theme and posting time from 2020 to 2022. The researcher used data on translation product from Machine Translation Instagram caption Indonesian to English. The researcher focuses on what types errors are found and What translation strategies are applied in Machine Translation caption from Cristiano Ronaldo Instagram. This is used to find the translation strategies of Cristiano Ronaldo Instagram caption and what types errors are found.
C. Formulation of the Problem
Based on the research background stated above, the subsequent problem formulation are proposed:
1. What translation strategies are applied in Instagram machine translation?
2. What types errors are found in from the machine translation of Cristiano Ronaldo Instagram captions?
D. Objective of the Study
The objectives of the research are:
1. To describe the Machine Translation error of the Cristiano Ronaldo Instagram captions.
2. To explain the translation strategies used in the Machine Translation of Cristiano Ronaldo Instagram captions.
E. Benefits of the Study
This is expected that the study will be beneficial for:
1. Theoretical Benefit
This research is expected to be an additional perspective and the results can be help of other researchers in related topics to contribute to the development of English literature.
2. Practical Benefit.
a. Student
The aim of this research is to increase knowledge of students, especially the translation department in translating text from the source language to the target language using translation strategies and the Error from Machine Translation. This research is expected to be an additional knowledge by English literary students in translating text based on machine translation error.
b. Other researchers
This research is expected to be an additional information about the related objects studied, especially on machine translation error.
c. Instagram Users
This research can be useful for Instagram users in terms of machine translation on Instagram, especially the Instagram account of Cristiano Ronaldo, Instagram users can find out how the machine translation on Instagram works.
F. Definition of the Key Terms 1. Translation
Translation is the process of converting languages that are equated from the source language into the target language by equalizing the true meaning (Catword, 1965).
2. Machine translation
Machine translation is the task of automatically converting source text in one language to text in another language (Brownlee, 2016).
3. Instagram
Instagram is “a relatively new kind of communication where people may easily publish their updates. It is a popular photo (video) taking and sharing mobile apps, with more than 150 million of registered users since its launch in October 2010” (Hu, Manikonda and Kambhampati, 2013). Instagram has released its machine translation, generally named Instagram Translate to help its various users understand each other despite the language barrier.
4. Caption
Caption, as defined by Oxford Advance Learner’s Dictionary, is
“words that are printed underneath a picture, cartoon, etc. that explain or describe it”.
5. Error
Coherence issues, other meaning shifts, grammar and structural issues are the machine translation error types that occur with multiple post-editing effort indicators.
Error Analysis. As a result, the approach is widely used to evaluate Translation Machines. This method counts the errors found in Cristiano Ronaldo Instagram caption that were caused by Instagram
Machine Translation. Counting these errors is, in most cases, the most practical method for obtaining useful translation machine information.
(Hutchin and Somers, 1992). This study's error analysis is based on the Keshavarz classification of error analysis.
Error in Translation is when the translation differs from the original text in terms of linguistic sense of meaning, whether at the word or sentence level, it is considered an error. The translation is labeled as an error if it is less precise or accurate than the source text, contains grammatical errors, or is less coherent or cohesive (Newmark 1988).
CHAPTER II LITERATURE REVIEW
A. Theoritical Background 1. Translation
Larson (1984) states that translation consists of studying the lexicon, grammatical structure, communication situation, and cultural context of the SL text, analyzing it so as to work out its meaning, then reconstructing the identical meaning using the lexicon and grammatical structure which appropriate within the target languages and its cultural context. Larson also says that translation has three steps they're studying the source text, analyzing it and reconstructing the meaning. Newmark (1988) states that translation is rendering the meaning of a text that is in the source language into the target language according to the intent of the author. Catford (1965: 1) states that the translation is the replacement of textual material from the source language (SL) to the equivalent of the target language (TL). That is, not all source language texts (SL) are translated into the equivalent of the target language (TL).
From these definitions above, it can be argued that translation is the process of reproducing, translating, or transferring the meaning of one language into another via the most natural equivalence potential.
Despite the fact that Nida and Taber include style as a significant criterion in translation, it is clear that the semantic or meaning equivalence are the important terms in these definitions. As a result,
these definitions agree that while reading a translation, TL readers should have the same response or meaning as SL readers.
2. Process of Translation
Figure 2.1: Machine Translation Process
The translation process according to Hutchins W J, Somers H L (1992), The direct approach is all for the analysis of the SL and resolve of SL ambiguity. Apart from SL analysis, the method allows the software to determine target language (TL) equivalence and study the TL syntactical structure. The method uses a machine or computer to duplicate the SL syntactical arrangement, separate the morphological inflections to get the base form, and match the SL and TL dictionaries' arrangements. This method uses a bilingual dictionary database as well as computer tools that can lexically and morphologically analyze and generate a document.
According to Hutchins W J, Somers H L (1992), Transfer based approach is when the software transforms the SL to the TL, this is the
stage. The first stage consists of translating the SL text to its intermediate form, which is molded as parse trees. The conversion of the representation to its TL equivalent is the second level. The software analyzes the SL abstract representation at this stage. The analysis varies from a syntactic to a semantic transfer. Parse trees that replicate the TL structure appear during the syntax transfer and convert the SL input. The map structures equivalents from the SL to the TL are used in the transfer process, and the results are adjusted according to the TL syntax and semantic bounds.
The generation of the TL is the last step. This method is commonly known to as the interlingua approach. The method is seen to be the most appropriate in a multilingual system. The method is divided into two stages: analysis (from the SL to its interlingua) and generation (from the SL to its interlingua) (from the interlingua to the TL). The substance of an SL sentence is syntactically and semantically evaluated throughout this procedure. As a result, the interlingua material is free of both SL and TL language structures. The content of the interlingua is referred to as the SL intermediate internal representation. A computer program that does language analysis generates the interlingua to the TL in construct it.
There are four types of machine translation according to Hutchins W J, Somers H L (1992), Statistical Machine Translation (SMT), Rule- based Machine Translation (RBMT), Hybrid Machine Translation, and Neural Machine Translation.
1. SMT employs statistical models that are based on the study of huge amounts of bilingual text. Its task is to find a match between a word in the source language and a word in the target languages.
2. RBMT, on either hand, uses grammatical rules to translate. To construct the translated sentence, it performs a grammatical analysis of the source and target languages. RBMT, on the other hand, needs significant proofreading and, due to its heavy dependence on lexicons, efficiency is only reached over a long period of time.
3. HMT is a combination of RBMT and SMT, as the term suggests. It makes use of a translation memory, which greatly improves the quality of the translation. However, HMT has its own set of flaws, the most significant of which being the requirement for intensive editing.
There will be a need for human translators.
4. NMT is a kind of machine translation in which statistical models for translation are developed using neural network models (based on the human brain). NMT's primary benefit is that it gives a single system that can be trained to understand both the source and target text.
3. Tanslation Strategies
The translation process is an activity that requires language understanding and analysis. There are three stages in processing the translation: analysis, transferring, and restructuring. This opinion was expressed by Nida (2001). In contrast to translation strategies, there are
some translation strategies applied to help the translator in translating the Source Text into Target Text.
In contrast to translation strategies, there are some translation strategies applied to help the translator in translating the Source Text into Target Text. There are seven translation strategies proposed by Vinay and Darbelnet (2000).
a. Borrowing
Borrowing is a translation strategy which involves using the same word, phrase or expression in the SL into TL without translation.
Borrowing is the most basic method of translation. Borrowing is usually used to bridge a gap, usually a metalinguistic one, in terms of new or unknown technical or conceptual concepts. Example in Indonesian, the word 'film' also becomes 'film.' For example, in the TL, the word 'menu' is translated exactly the same as 'menu' in Indonesian.
b. Calque
Calque is a translation strategy in which an expression in the SL is translated word-by-word into the TL. Calque is a special type of borrowing whereby a language borrows an expression from another, but the translator translates literally each of its parts. The equation is that calque and borrowing both translate words by translating their lexical and structural parts. The lexical element signifies the real meaning of the word. Meanwhile, structural means word arrangement.
So, in other words the translation of the calque is depending on the meaning of the word and its structure. For example:
ST: Perdana menteri TT: Prime Minister
From the translation of this phrase, we can see that the lexical element in both sentences is still kept.
c. Literal translation
Literal translation is a strategy of translating straight SL word by word grammatically and appropriate in the TL. Literal translation is acceptable if the translated language has the same word, phrase, or sentence structure, meaning, and style as the SL. This strategy is not required to make changes other than clarify, such as those regarding grammatical suitability. The objective of this strategy is to translate words that have the same lexical elements and the same sentence structure. For example:
ST: Saya memberi pidato di sekolah itu tahun lalu TT: I gave a speech at the school last years,
It can be understood that besides translating lexically, literal translation also translates words by modifying the structure of the TL.
d. Transposition
Transposition: substituting one word class with another without changing the meaning of the message. Transposition is a strategy of changing the word class of a word or particular word combination without affecting the meaning in translation process.
Transposition is almost the same as Calque's strategy. But the difference is, the Calque only translates words and phrases.
Meanwhile, transposition can modify more than only phrases, but start from grammatical structures, change the position of adjectives or even change the plural form to singular. For example:
SL: Red car TT: mobil merah
This change was made to adjust the grammatical elements in the TL.
e. Modulation
Modulation: variations of the message form, obtained from a change in point of view. Modulation is a translation strategy that happens in the translation process which involves changing the point of view of the SL in the TL in the semantic arena. Modulation is the varying of the language, produced by a change in the point of view, this change could be justifiable, although literal even transposed.
f. Equivalence
Equivalence: rendering two situations by different stylistic and structural methods; these two texts contain the source text and its similar text which is the target text. For examples:
‘Bookworm’ becomes ‘Kutu buku’
‘In the same boat’ to ‘Senasib’
g. Adaptation
Adaptation strategy is used when certain word, phrase or expression in the SL is not suitable or acceptable culturally in the TL.
The translator must replace the word, phrase, or expression in the SL by adapting the suitable or acceptable word, phrase, or expression of the TL. Furthermore, it is extremely limit of translation which is used in cases the translator needs to create a new situation that may be considered equivalent. For example:
‘Children of nations’ becomes ‘Anak segala bangsa’
4. Error Analysis in Machine Translation
Hutchins and Somers argue that one of the most helpful and practical approach to evaluate MT in by counting the error to identify the amount of work on revision or correction required in the raw output.
Commonly, error analysis is performed by counting “each addition or deletion of a word, substitution of one word by another, and instance of the transposition of words in phrases” (1992:164). (1992:164). The outcome of the counting then will be calculated into the errors or revised words’ percent in the whole text. providing the alternative approach for the error is also important for the researcher to conduct the comparison in finding the errors.
Hutchins and Somers point out that to solve this problem, error analysis required to be done based on a “classification of errors by kind of linguistic phenomena and by relative difficulty of correction”
(1992:164) and subsequently the means to measure the performance of the MT. Therefore, error analysis in assessing MT cannot be done only by counting the errors. It also has to be based on certain operational categories to limit the possibility of subjective account in identifying the errors.
5. Errors Classification in Machine Translation
Regarding the problem of subjectivity in applying error analysis in measuring machine translation quality, Hutchins and Somers as indicated above, advise that there has to be a classification of errors in the assessment. Some existing classifications of errors are based mostly on the fluency of forms and correctness of the contents. In her journal, Assessing Machine Translation Quality with Error Analysis (2010: 1-12), Koponen proposes the idea of using error analysis to assess machine translation output in terms of semantic content accuracy (translation errors) rather than only the fluency of forms and accuracy of contents (language errors).
Koponen (2010) defines mistakes category based on the tested text into. they are namely relation between source. All examples are taken from Erling Haaland's Instagram machine translation product
. The mismatches are as follows:
a. Omitted Concept
It happens when Source Text (ST) concept that is not conveyed by the Target Text (TT). It means the concept that should appear in TT is lacking. For example:
ST: Hanging around, acting cool TT: Berkelana, bertingkah, the concept of cool is omitted.
b. Added Concept
It exists when TT concept that is not exist in the ST.
This category is totally the reverse of Omitted Concept. TT introduces new concept that is absent in ST. For example:
ST : No words can describe this feeling!
TT: Tidak ada kata yang bisa menggambarkan itu perasaan ini! From the TT,
The additional of the word “itu” is categorized as Added Concept.
c. Mistranslated Concept
It happens when the incorrect selection of terms in a specific context, the wrong arrangement of terms or the literal translation of a term in the target text.
Example: ST: That magic moment TT: kesan magis itu
From the example above the word Magis is a mistranslated concept.
d. Untranslated Concept
It is when a source language word that appears in the target text or the use of recent loan words.
Example: ST: Amazing win team Looking forward to the next game!
TT: Tim pemenang yang luar biasa! Menantikan game berikutnya!
Game in ST is untraslate e. Substituted Concept
It is when TT concept is not a direct lexical equivalent for ST concept but can be considered as a valid replacement for the context.
Example: ST: Important victory at home! Now all focus on @championsleague
TT: kemenangan penting di kandang! Sekarang semua fokus pada @championsleague
Home translated in to Kandang that is valid replacement f. Explicitated Concept
It is when the TT concept explicitly states the information left implicitly in the ST without adding any information. Example:
ST: Glad to have been honored man of the match yesterday #UCLMOTM
TT: Senang mendapat kehormatan sebagai man of the match kemarin #UCLMOTMA
Man of the match is Explicated
After the data had been collected the researcher one group of errors categories, errors related to individual concepts. The errors category were based on theory Omitted Concept, Added Concept, Mistranslated Concept, Untranslated Concept, Substituted, Explicitated Concept. created by Koponen (2010). Here was the table used in the analysis. From the theory above, the researcher decided to use the Relation between source and target concepts because this method is suitable for use in this study because it is related to Source and Target concept.
B. Previous Related Studies
In this study, the researcher refers to previous research related to directive speech acts in both journals and theses. Some of the studies that are relevant to this research are as follows:
Kurnianto’s who writes “Google Translate Assessment with Error Analysis: An Attempt to Reduce Errors” Kurnianto’s undergraduate thesis focuses on the errors found in Google Translate’s performance. He looks through three texts: a lease agreement, a National Geographic article, and an iPad user guide that has been translated from English to Bahasa Indonesia.
Kurnianto also uses Koponen’s error classification to classify errors into the
proper types of concept errors, which include Added Concept, Omitted Concept, Mistranslated Concept, Untranslated Concept, Substituted Concept, and Explicitated Concept. He confirms his recommendation for decreasing mistakes by mentioning the kinds of errors discovered.
Kurnianto uses Farr's idea and the trial-and-error method to get the data.
Kurnianto concludes by displaying the amounts of mistakes discovered and claiming that the most and least common forms of errors exist.
Febriana’s who writes “The Translation Performance of Sederet.com and Google Translate: A Comparative Study with Error Analysis”
Febriana’s thesis compares the performance of Google Translate and Sederet.com in translating three short stories: "Dad's Blessing," "The Grasshopper and the Ant," and "The Princess and the Pea." Her thesis is sought to identify errors in the translations of those three short pieces. As a result, she decides to focus on her analysis using Koponen’s error categories.
The similarity between this study and Kurnianto’s is the researcher also only uses the individual concept error of Koponen to assess the quality of TMs. The researcher also considers the numerical data as one component to determine the conclusion of the performance of each TM. The difference of this thesis with Kurnianto’s is the researcher’s aim is not to look for the technique to reduce errors but to focus on comparing the TMs performance.
In addition, another different is the researcher employs two TMs, Google Translate and Bing Translator, although the previous thesis only focuses on Google Translate.
Ariany’s who writes “Bing Translator’s and Google Translate’s Performance in Translating English Literary and Academic Texts into Indonesian” Ariany’s thesis compares the performance of Google Translate and Bing Translator in translating two different kinds of material: academic text from Peter Barry's "Feminism and Feminist Criticism" and literary text from Hemingway's Cat in the Rain. The goal of this study is to determine the errors caused by each TM and compare their performance. The results of the analysis are then used to assess Google Translate and Bing Translator's performance. The similarity between this study and Ariany’s is the researcher also only uses the individual concept error of Maarit Koponen.
CHAPTER III RESEARCH METHOD
The researcher tries to show how to do research in this chapter. The researchers can examine the study's research method. It helps in the collection of accurate data and the analysis process required to answer and solve the research problem. The research design, data and data sources, research instruments, sampling technique, data collection, data validation, and data analysis are all covered in this chapter.
A. Research Design
According to Creswell (2009: 3), research design is plans and the procedures for research to detailed methods of data collection and analysis.
Based on the research purposes this research used descriptive qualitative research method. The researcher found some data and then collected, classified, analyzed the data and made it into conclusion. this research used a descriptive qualitative method because the purpose of this study is to describe the phenomena of translation, especially the error on machine translation which are used in Instagram captions. This research is a qualitative study because the data is not statistical data. but there are also calculations that are used only found error of the translation.
The researchers analyzed Translation Strategies and Machine Translation error on Instagram captions. In this study the researchers focused on the error obtained from the machine translation process. Then analyze the results of machine translation on Instagram, and chose what
strategies are used of Instagram machine translation. This study aims to find error in Cristiano Ronaldo Instagram Captions with machine Translation on Instagram. For collecting data, the researcher chooses one of Cristiano Ronaldo post on Instagram since the focus was to compare the performance of different Machine Translation in translating text from English into Indonesian. The choose post is post from Cristiano Ronaldo Instagram posting with football theme. Second, click one translate feature on Instagram caption with Source text and Target text. The researcher draws conclusions from what an error in the target text and what strategies of the translation using machine translation.
B. Data and Source of Data
The data collection steps include setting the study’s limits, gathering information via unstructured or semistructured observations and interviews, documents, and visual materials, and defining the technique for capturing information are all steps in the data collection process, according to (Creswell, 2014).
The objective data collected in this research were taken from a ST and TT. The data is screenshot from Cristiano Ronaldo Instagram post and machine translation product in the caption, were captions in @Cristiano Instagram (https://www.instagram.com/cristiano) account with the most Instagram user followers, which were collected from January 2018 until May 2022. The TT were the translation which had been translated by
Instagram Translate. The data is a screenshot from Cristiano Ronaldo Instagram caption before and after translation process.
C. Research Instruments
The research instrument plays the main role in completing the data to get the result of the research. According to Arikunto (2000), an instrument is a tool used by researchers in collecting data to make it more systematically and easily. This includes the main and supporting instruments. States that research instruments can be used to show the validity and reliability of the instrument, the individual who developed them, and the permission required to use them Creswell (2008).
The main instrument of the research is the researcher. This is because the researcher that decides and collect the data. The supporting instruments are tools that researchers employ to help them analyze research and work with results. Pens for writing, books for writing, laptops for collecting data, classifying data, and conducting research, papers for printing, smartphones for finding data, theories of some experts, a dictionary book to find words that the researcher does not know, and an electronic dictionary on the smartphone to find words that are not in the dictionary book until finding the result are the supporting instruments for this research.
D. Data Collection Techniques
To collect data for this research, a documentation technique is used.
Documentation is the systematic review of documents, both printed and electronic (computer-based and internet-based) (Bowen, 2009). To understand the data, the documentation technique requires analyzing and examining data (Moleong, 2010). These data research are form of images and texts from the post of social media Instagram. As a result, documentation techniques make it easier for the researcher to find data and analyze data based on the research objective, specifically to understand the translation strategies and machine translation error analysis used on the object. As a result, the researcher can make conclusions from the data.
Based on the content analysis, the researcher obtains data from Cristiano Ronaldo Instagram posts using translation strategies and machine translation error analysis. The data is presented in the form of a caption. The researcher must complete one step in data collection. This study's data collection technique is provided in the following steps:
1. The researcher open Instagram application on phone;
2. The researcher searches Cristiano Ronaldo Instagram account the posts 2020 until 2022;
3. The researcher classifies the data and no data translation strategies based on Vinay and Darbelnet (2000);
4. The researcher Identifies and collects the data machine translation error analysis based on Maarit Koponen theory; and 5. The researcher gives codes on the collected data.
The data of this research can be easier to analyze by giving the codes. The codes of the data that given to the data is according to the number of the datum, the translation strategies and the machine translation error analyze. To make a classification of the data analysis, the researcher gives codes to each data. The form of the codes can be seen below:
Num/ST/TT/TS/EA
Num : the data number 1,2,3, etc.
ST : Source Text TT : Target Text
TS : Translation Strategies EA : Error Analyze
Translation Strategies B : Borrowing C : Calque
LT : Literal translation M : Modulation E : Equivalence A : Adaption T : Transposition
Classification of Machine Translation error analyze A : Adaption
OC : Omitted concept
AC : Added concept
MC : Mistranslated Concept UC : Untranslated Concept SC : Substituted Concept EC : Explicitated Concept As examples the codes of the data:
Datum 61/ST/TT/M/MC means that the datum number is 61, source text, target text and classified modulation strategies and MC mean mistranslated concept.
6. Analyze the data to answer the problem statement
7. Draw the conclusion from the result of the analysis and give suggestions
E. Technique of Analyzing Data
According to Kothari (2004), data analysis is the act of organizing, manipulating, and considering the value of data collected. The researcher systematically organizes the data by coding it into categories and creating matrixes in order to add order, structure, and explanation to the acquired data. Data analyzing techniques describes the processes taken by the researcher to analyze the data. The data is analyzed using descriptive analysis, and the data is explained using the researcher's own words.
In order to achieve the objectives of this research, the data is analyzed using descriptive analysis, and the data is described using the
researcher's own words. The researcher using Spradley's theory in analyzing the collected data (1980).
Analysis of qualitative data based on Spredley: 1980.
1. Domain analysis
Domain analysis techniques are a step that focuses on something comprehensive and generic (Sugiyono, 2017). As a result, researchers must dig deep to find what they're searching for in the field they're discussing and studying. This analysis is quite wide, and it will need to be narrowed down into the domain of research in order to get a solid result. In this case, the researcher focuses on the forms of Translation strategies and the error classification Instagram Machine Translation when collecting data.
2. Taxonomy analysis
After the determination of research data, the next stage in data analysis technique is taxonomy analysis. Taxonomy is the classification and grouping of data for the purpose of determining the specificity of each bit of data. The researcher uses data coding to classify the data in this stage. The collected codes must be organized according to the codes that have the same form of data.
Table 3.1 The Taxonomy Data
Translation Strategies
Borrowin g (B)
Calqu e (C)
Literal Translatio
n (LT)
Transpositio n (T)
Modulatio n (M)
Equivalenc e (E)
Adaptatio n (A)
3. Componential analysis
The next step is componential analysis. Componential analysis is the technique of analysis investigates cause and effect. In this step, the researcher uses componential table to know what the translation strategies and error classification Instagram Machine Translation.
Machine Translation Error Categories
Omitted Concept (OC)
Calque (C)
Added Concept
(AC)
Mistranslated Concept (MC)
Untranslated Concept (UC)
Explicitated Concept (EC)
Table 3.2. Componential Table Analysis
No.
Translation Strategies
Machine Translation Error Categories
OC AC MC UC SC EC
1. B: Borrowing
2. C: Calque
3. LT: Literal Translation
4. T: Transposition
5. M: Modulation
6. E: Equivalence
7. A: Adaption
Explanation of componential table:
OC : Omitted Concept AC : Added Concept MC : Mistranslated Concept UC : Untranslated Concept SC : Substituted Concept EC : Explicitated Concept B : Borrowing
C : Calque
LT : Literal translation M : Modulation
E : Equivalence A : Adaption T : Transposition 4. Discovering cultural theme
The researcher used cultural theme analysis to convey the conclusion from the dominant data that have been found at Machine Translation product Cristiano Ronaldo Instagram Caption. The researcher establishes the dominant data in the componential table based on the problem statements. The cultural theme obtained is in the form of the translation strategies. This is because the translator tries to present the translation as detailed as possible by using words that are easy to understand, so that the target reader knows the type of error are found.
The theme of this study will be the use of Translation Strategies by Machine Translation of Cristiano Ronaldo Instagram Caption
F. Data Validation Techniques
Data validation is an integral part of any research approach, both qualitative or quantitative. This is because using data collection strategies to gather information, analyze data, or prepare to display our data is intimately connected to data validation. Data is the most important thing in this research. Before the researcher conducts the analysis, the data must be validated first. Creswell (2009: 352) states that the validity of qualitative method is that researchers must test the accuracy of the data. What is meant by accuracy here is that the data must be completely valid in order to
produce significant results. According to Creswell and Miller (2000), there are three forms of data validation, namely triangulation, member of checking and auditing. Triangulation is a form of data validation that is used based on more than one individual.
From the explanation, this research uses the source of data in observing the process of documentation of collecting the data from Cristiano Ronaldo Instagram account. After collecting the data, then the researcher analyzes the data based on the theory in the chapter II. For the last, the data will be validated with a validator to check the data validity.
In this case, the researcher discusses the data analysis with the research advisor, with Arkin Haris, S.Pd., M.Hum., an English educator and translation expert, to ensure that the data is valid. He will be the expert in charge of verifying the validity of the research’s data.
CHAPTER IV
FINDING AND DISCUSSION
This chapter is divided into two points which will be discussed. There are research findings and discussions. In these research findings the result of research analysis will be discussed, and divided into two sub-chapters, the first discusses machine translation error types as found. Then the second discusses the translation strategies found in the data. Then the discussion section will provide the depth and detailed description of the data findings.
A. Research Findings
In this study, there are two questions. They are: 1) What translation strategies are applied in Instagram machine translation? 2) What types errors are found in from the machine translation of Cristiano Ronaldo Instagram captions? This research data comes from Cristiano Ronaldo Instagram caption. In this research the researcher uses Vinay and Darbelnet’s theory (2000). There are seven technique that have been classified on the theory such as name calling Borrowing, Calque, Literal translation, Transposition, Modulation, Equivalence, Adaptation. In this research the researcher uses Maarit Koponen’s theory (2010). There are six technique that have been classified on the theory such as name Omitted Concept, Added Concept, Mistranslated Concept, Untranslated Concept, Substituted, Explicitated Concept.
Tabel. 4. 1 Translation Strategies Finding
No. Translation Strategies
1 B: Borrowing 5
2 C: Calque 17
3 LT: Literal Translation 46
4 T: Transposition 7
5 M: Modulation 2
6 E: Equivalence 0
7 A: Adaption 3
Total 80
1. The translation strategies empoyed in Cristiano Ronaldo Instagram caption
What translation strategies are applied in Instagram machine translation? According to Nida (2001), translation process is an activity that requires language understanding and analysis. There are three stages in processing the translation: analysis, transferring, and restructuring. In this research the researcher uses Vinay and Darbelnet’s theory (2000).
There are seven technique that have been classified on the theory such as name calling Borrowing, Calque, Literal translation, Transposition, Modulation, Equivalence, and Adaptation.
The researcher classified the data based on the translation strategies theory Vinay and Darbelnet (2000). There are seven techniques that have been classified in the theory, such as name calling, borrowing, caching, literal translation, transposition, modulation, equivalence, and adaptation. The second part, the researcher classified the data based on the error classification in machine translation theory Maarit Koponen (2010). The researcher found 80 data. The translation strategies include borrowing strategies with 5 data, calque strategies with 17 data, literal translation with 46 data, Transposition with 7 data, modulation with 2 data, and adaptation with 3 data. Literal translation is more dominant because the Instagram machine translation works by translating word to word sequentially and according to language arrangement.
a) Borrowing
Borrowing is a translation strategy which involves using the same word, phrase or expression in the SL into TL without translation.
Borrowing is the most basic method of translation. Borrowing is usually used to bridge a gap, usually a metalinguistic one, in terms of new or unknown technical or conceptual concepts.
1) Datum 31/ST/TT/B/UC
Source Text : Faster...@nikefootball Target Text : Faster...@nikefootball
In the translation above, the machine translation used borrowing procedure in translating word Faster from ST into TT. The caption faster is mean to an endorsement from NIKE brand. The word should be translated to word to “lebih cepat” in this in this case the Instagram machine translation did not translate it because the symbol
“…..” make the machine translation can not read the text. That menas instagram machine translation did not read the rule of the word. The word “@nikefootbal” still same and without translated is correct because that is @ is a symbol to summon an account.
2) Datum 44/ST/TT/B/UC Source Text : Caption….
Target Text : Caption….
In the picture above the caption “caption” is mean Cristiano want the followers make a caption. In the translation above, the machine translation used borrowing procedure in translating word
“Caption…” from ST into TT. The word “Caption” if translated into TL meant “Gaya Kamis”. In this chase the Instagram machine translation did not translate it because the symbol “…..” make the machine translation can not read the text. Machine translation in instagram
b) Calque
Calque is a translation strategy in which an expression in the SL is translated word-by-word into the TL. Calque is a special type of borrowing whereby a language borrows an expression from another, but the translator translates literally each of its parts. The equation is that calque and borrowing both translate words by translating their lexical and structural parts. The lexical element signifies the real meaning of the word. Meanwhile, structural means word arrangement.
So, in other words the translation of the calque is depending on the meaning of the word and its structure.
1) Datum 34/ST/TT/C/MC
Source Text : Felling good and healthy Target Text : Jatuh baik dan sehat
In the post above Cristiano want to tell to his follower he is healthy, and in the translation above, the machine translation used calque procedure in translating “Felling good and healthy” into “Jatuh baik dan sehat”, the word Felling is a calque because the word felling does not follow from the sentence before it and make this translation product Calque. In this case the machine translation is the lexical element signifies the real meaning of the word, translated word-by- word into the TT.
2) Datum 67/ST/TT/C/MC
Source Text : Get back and break again!
Target Text : Dapat kembali dan istirahat lagi!
In the translation above, the machine translation used calque procedure in translating “Get back and break again! ” into “Dapat kembali dan istirahat lagi! ”, in this case the machine translation translates words Get into dapat, and the word Get not follow from the sentence before it and make this translation product Calque means that the machine translation translates word by word by translating their lexical and structural parts, translated word-by-word into the TT.
c) Literal translation
Literal translation is a strategy of translating straight SL word by word grammatically and appropriate in the TL. Literal translation is acceptable if the translated language has the same word, phrase, or sentence structure, meaning, and style as the SL. This strategy is not
required to make changes other than clarify, such as those regarding grammatical suitability. The objective of this strategy is to translate words that have the same lexical elements and the same sentence structure. The grammar of each language is taken into account by the translation machine, that also uses rules, examples, and grammar to transfer the grammar of the source language (SL) into the target language (TL). In order to translate from SL to TL, TM refers to previously recorded rules, examples, and grammars (Alawneh 2011).
1) Datum 3/ST/TT/LT/OC
Source Text : The winners are who keep trying for the best!
Target Text : Pemenang adalah yang terus berusaha yang terbaik!
In the picture above, it was clear that the sentence “The winners are who keep trying for the best!” in source text which is translated into “Pemenang adalah yang terus berusaha yang terbaik!”
the target text is translated precisely without changing the form or word order. The literal translation process had an impact on how well the meaning and idea of the translated text were delivered to Instagram users. Instagram machine translation work grammarly to transfer the grammar of the source language (SL) into the target language (TL).
2) Datum 17/ST/TT/LT/MC
Source Text : Wasn't the result we wanted, but now it’s time to recover well and focus on the next game!
#mufc
Target Text : Bukan hasil yang kita cari, tapi sekarang saatnya pulih dengan baik dan fokus ke pertandingan selanjutnya! #mufc
In example above, the sentence uses literal translation to translate the sentence “Wasn't the result we wanted, but now it’s time to recover well and focus on the next game! #mufc” in source language which is translated into “Bukan hasil yang kita cari, tapi sekarang saatnya pulih dengan baik dan fokus ke pertandingan selanjutnya! #mufc” that mean doesn't go out of context in target language. The researchers concluded the machine translation transfer source language grammar and word order, as well as all of the source language words' primary meanings. However, because the literal translation procedure affected this translation for giving the message from source language, the sentence above is translated literally without any reduction or addition in order to keep the equivalences.
