The Use Of Google Translate In Translating The Afrikaans Language Into English.

Nhận Làm Báo Cáo Thực Tập Thuê Trọn Gói –
Điểm Cao
Zalo/Tele Nhắn Tin Báo Giá : 0909.232.620
MINISTRY OF EDUCATION AND TRAINING
DUY TAN UNIVERSITY
DEPARTMENT OF FOREIGN LANGUAGES
GRADUATION PAPER
NGUYỄN THỊ DIỄM TRINH
THE USE OF GOOGLE TRANSLATE IN
TRANSLATING THE AFRIKAANS
LANGUAGE INTO ENGLISH
Code : …….
Course : ……..
NGUYỄN
THỊ
DIỄM
TRINH-
THE
USE
OF
GOOGLE
TRANSLATE
IN
TRANSLATING
THE
AFRIKAANS
LANGUAGE
INTO
ENGLISH

Điểm Cao
HCHC – DEC 2019

Điểm Cao
MINISTRY OF EDUCATION AND TRAINING
DUY TAN UNIVERSITY
DEPARTMENT OF FOREIGN LANGUAGES
GRADUATION PAPER
THE USE OF GOOGLE TRANSLATE IN
TRANSLATING THE AFRIKAANS
LANGUAGE INTO ENGLISH
Code : …….
Course : ……..
SUPERVISOR: NGUYỄN THỊ VIỆT NGA
STUDENT : NGUYỄN THỊ DIỄM TRINH
HCMC – DEC 2019

Điểm Cao
TABLE OF CONTENTS
CHAPTER 1. INTRODUCTION ....................................................................... 1
1.1. Rationale................................................................................................... 1
1.2. Aim and Objectives of the Study ............................................................. 2
1.2.1. Aim...................................................................................................... 2
1.2.2. Objectives............................................................................................ 2
1.3. Research Questions .................................................................................. 3
1.4. Scope of the Study.................................................................................... 3
1.5. Significance of the Study.......................................................................... 3
1.6. Organization of the thesis......................................................................... 4
CHAPTER 2 ....................................................................................................... 5
THEORETICAL BACKGROUND.................................................................... 5
2.1. Translation theory..................................................................................... 5
2.1.1. Definition of translation...................................................................... 5
2.1.2. Translation methods............................................................................ 6
2.1.3. Translation shifts................................................................................. 8
2.1.4. Translation equivalence .................................................................... 10
2.1.5. Translation errors.............................................................................. 12
2.1.6. Translation Quality Assessment (TQA)............................................ 19
2.2. Machine translation ................................................................................ 22
2.2.1. The history/development of machine translation.............................. 22
2.2.2. Google Translate............................................................................... 23
2.3. Genres..................................................................................................... 27
2.3.1. Definition of genre............................................................................ 27
2.3.2. Classification of genre. ..................................................................... 28
CHAPTER 3. RESEARCH METHODS .......................................................... 29
3.1. Research methods................................................................................... 29
3.2. Data collection........................................................................................ 30
3.3. Data analysis........................................................................................... 31

Điểm Cao
CHAPTER 4. FINDINGS AND DISCUSSIONS ............................................ 34
4.1 The frequency of errors in literal text and technician text ...................... 34
4.2. Article..................................................................................................... 40
4.3. Pronoun................................................................................................... 41
4.4. Google improvements ............................................................................ 42
CHAPTER 5. CONCLUSIONS AND IMPLICATIONS ................................ 45
5.1 Summary of the findings ......................................................................... 45
5.2 Implications for translation...................................................................... 46
5.3. Limitations of the study.......................................................................... 46
5.4. Suggestions for further studies ............................................................... 47
REFERENCES.................................................................................................. 48

1
CHAPTER 1. INTRODUCTION
1.1. Rationale
This increase in global business has provided a greater and more
urgent need for direct translation which is available within a short time.
An obvious tool which has been created to help accommodate this need is
Google Translate, which is known for free online machine translation.
Google Translate (GT) has become an instrument in machine translation
that has been claimed by its provider to be developing at great pace to
achieve ever higher degrees of accuracy. Because GT is freely available
on the Internet and has its own app on computers, tablets and
smartphones, it is accessible anywhere the Internet and Google services
are available, and it easily enables users to render a text in one language
into over 100 other languages with outcomes of varying quality and
comprehensibility.
Even though Google Translate is a very popular tool used by
millions of Internet users and it has improved a lot so far, it is clearly not
perfect yet. Therefore, we cannot rely on it blindly without knowing how
accurate it is. So this study would be very necessary for users to know for
which genre they can easily get good results and which genre they would
get less accurate results. That would help users to decide how much trust
they can put on GT when they need to translate texts that belong to
different genres such as tourism news, medical information, and the short
story.
I find some related s studies which have been carried out recently
such as Aiken, M., and Balan, S. (2011) with An Analysis of Google
Translate Accuracy in Translation journal. The aim of the research was
to compare the quality of Google Translate considering the direction of

2
translation and providing enough insight for the errors made by Google.
Aiken, M., Ghosh, K., Wee, J., & Vanjani, M. (2009) with An Evaluation
of the Accuracy of Online Translation Systems in Communications of the
IIMA. This study gives an estimate of how good a potential translation
might be using GT. The result of this study shows that translations
between European languages are usually good, while those involving
Asian languages are often relatively poor. Further, the vast majority of
language combinations probably provide sufficient accuracy for reading
comprehension in college. Several studies on the use of Google Translate
in translation focused on computational analysis. For example, Rensburg,
et al. (2012) investigates “the use of Google Translate in translating the
Afrikaans language into English”. Jia et al. (2012) conducted a study
investigating “the use of machine translation (MT) to translate the
selected titles of Chinese papers from a Chinese journal into English”.
Moreover, there have been a lot of master theses carried out based on the
light of machine translation. Most of them focus on the comparison
among machine translations or between machine translations and human
translations in different languages. There has not been any study of the
accuracy that GT can achieve in translating translates literary texts, and
technical texts. This study is hoped to fill in this research gap.
1.2. Aim and Objectives of the Study
1.2.1. Aim
This study aims to investigate the quality of Google Translate’s
translations of literary texts and technical texts from English into
Vietnamese.
1.2.2. Objectives
To fulfill the above aim, the researcher will:

3
- evaluate the quality of Google Translate’s Vietnamese translations
of English literary texts and technical texts.
- compare the quality of the translations of the literary texts and that
of technical texts in order to see which of these two genres Google
Translate translates better.
1.3. Research Questions
In order to achieve the above aim and objectives, the researcher will
collect data and analyse it in order to answer the following questions:
1. How does Google Translate translate literary texts and technical
texts in terms of translation quality?
2. Which of these two genres does Google Translate translate
better?
1.4. Scope of the Study
This study examines the quality of the translations carried out by
Google Translation in two genres, including literary texts and technical
texts. Other genres are not examined in this study. Only the quality of
English-Vietnamese translations of texts of these two genres will be
investigated. For each genre, from 2 to 3 texts of about 4000 words in
total will be translated by Google Translate.
1.5. Significance of the Study
The study is conducted to make significant contributions in terms
of both theoretical and practical aspects of the topic. For practical, the
results of this study may be useful for translators when GT translates a
document in English into Vietnamese or the other way round. The
findings of this study may also be useful for teaching and learning
English in general and translation in particular. We hope this study will

4
provide the accuracy of GT for the translators when doing the translation
by using GT.
1.6. Organization of the thesis
The thesis will be organized into five chapters. Chapter 1
(Introduction) addresses the rationale, aim and objectives, research
questions, the significance of the study, the scope of the study, and the
organization of the study. Chapter 2 (Literature review) presents an
overview of the translation theory, translation methods, translation shift,
translation quality assessment, translation equivalence, errors in
translation, machine translation, Google translation, genres. Chapter 3
(Methods) addresses the research methods, description of the procedures
for data collection and data analysis. Chapter 4 (Findings and
discussions) reports and discusses the results of data analysis. Chapter 5
(Conclusion) summarizes the major findings and offers implications for
practice and further studies.

5
CHAPTER 2
THEORETICAL BACKGROUND
In this chapter, the theoretical preliminaries of the study will be presented,
including an overview of Google Translate, concerning its definition,
classifications, identification, and the translation errors.
2.1. Translation theory
2.1.1. Definition of translation.
Translation is defined in many ways and may be understood differently.
Translation is one of the diverse means of communication and, the most
important one. This is mainly because it sets up an association between at least
two languages and their culture. Through translation, the characteristic elements
from one language are also transferred into the other. Translation focuses on the
translator’s role, from taking a source text and turning it into one in another
language, and it also concentrates on the specific product created by the
translator.
From the perspective of Carford (1965), translation means "the
replacement of a textual material in one language (SL) by equivalent textual
material in another language" (p. 20). It means that translation is the
replacement of the word, and grammar structures in the ST with the equivalent
wordsand grammar structures in the TL. According to Nida and Taber (1982, p.
12), “translating consists in reproducing in the receptor language the
closest natural equivalent of the source-language message, first in terms
of meaning secondly in terms of style”. From the explanation above, translation
can be simply defined as transferring the message from the SL into the TL in
terms of meaning and style. Hatim and Mason (1990, p. 3), on other hand, focus
more on the communication purpose of translation saying that “translation is

6
communicative process which takes place within social context.” Newmark
(1988) simply defines translation “is rendering the meaning of a text into
another language in the way that the author intended the text” (p. 5).
These definitions, in spite of slight differences in expression, share a
common thing which is to find equivalents that best or appropriately of the
target language‘s lexical and grammatical structures, cultural context,
communication‘s situation which have similar characteristics of the original.
2.1.2. Translation methods
Newmark (1988, pp. 45-53) proposed 8 translation methods showed in
the following diagram.
Newmark’s flattened V diagram
SL emphasis TL emphasis
Word-for-word translation Adaptation
Literal translation Free translation
Faithful translation Idiomatic translation
Semantic translation Communicative translation
(Newmark, 1985, p. 45)
Following are the main features of these translation methods.
- Word-for-word translation: This method is often demonstrated as
interlinear translation. In this method, the SL word order is preserved and the
words are translated singly by their most common meanings, out of context.
Cultural words are translated literally. The main use of this method is both to
understand the mechanics of the source language and construe a difficult text as
a pre-translation process.

7
- Literal Translation: In this method, the SL grammatical constructions
are converted to their nearest TL equivalents. However, the lexical words are
again translated singly, out of context. This method helps indicate the problems
to be solved in the translation process.
- Faithful translation: Following this method, the translator tries to
reproduce the precise contextual meaning of the original text within the
constraints of the TL grammatical structures. S/he transfers cultural words and
preserves the degree of grammatical and lexical “abnormality” (i.e., deviation
from SL norms) in the translation. S/he tries to be completely faithful to the SL
writer’s intention and the text-realisation.
- Semantic translation: This translation method differs from faithful
translation in that the translator has to take more account of the aesthetic values
of the SL text.
- Adaptation: Newmark (1988) said that this is the “freest” form of
translation. This method is mainly used to translate plays and poems. In this
method, the SL culture is converted to the TL culture, and the text is rewritten.
- Free translation: Following this method, the translator tries to produce
the TL text ignoring the style, the form, and even the content of the original.
S/he paraphrases the original text using the target language. Therefore, the TL
text is usually longer than the original, and the TL text is called an “intralingual
translation”.
- Idiomatic translation: In this method, the translator reproduces the
message of the original text, but s/he tends to distort the aspects of meaning
owing to his or her preference of colloquialisms and idioms which do not exist
in the original text.
- Communicative translation: Following this translation method, the
translator attempts to reproduce the exact contextual meaning of the original
text in a way that both content and language are readily acceptable and
comprehensible to a TL readership.

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2.1.3. Translation shifts.
According to Catford (1965, p. 73), shift in translation means departure
from formal correspondence in the process of going from a source language to a
target language. In addition, a shift is said to occur if, in a given target text, a
translation equivalent other than the formal correspondent occurs for a specific
source text element. According to Bell (1993), to shift from one language to
another is, by definition, to alter the forms. From the previous explanations
about translation shift, it can be concluded that translation shift is used to
describe the changes occurs between the source text and the target text. Catford
(1965) states that there are two major types of shift: level shifts and category
shifts.
Translation shift
Level shifts Category shift
Structure
shifts
Class
shifts
Unit shifts Intra -
system shift
+ Level shift: Catford (1965, p. 73) speaks of a level shift when a source
text item has a textual equivalent on a different linguistic level. Level shifts,
however, can only occur between the levels of grammar and lexis. This
restriction according to to Catford’s understanding of translation equivalence
from his structuralist point of view, is not based on a sameness of meaning, for
meaning is defined as “the total network of relations entered into by any
linguistic form” (Catford, 1965, p. 35 ) and consequently cannot be the same
across languages.

9
The second categorization is category shift. Category shifts are departures
from formal correspondence in translation (Catford, 1965, p. 3 ). Further,
Catford classifies category shifts into four subtypes:
+ Structure shift: A structure is defined as the patterned way in which a
unit is made up of lower-rank units. A structure shift thus occurs when the
target structure contains different classes of elements or when it contains the
same classes of elements but arranges them differently. According to Catford
(1965, p. 77), structure shifts are the most frequent among category shifts. As an
example, Catford presents the translation of an English clause consisting of the
elements of subject, predicate, and complement, into a Gaelic clause that is
composed of the elements of predicate, subject, complement, and adjunct.
+ Class shift: We define a class as that grouping of members of a given
unit which is defined by operation in the structure of the unit next above. Class
shift, then, occurs when a TL item is a member of a different class from that of
the original item. (e.g., a noun in the ST is translated into the TT using a verb).
+ Unit shift: By unit-shift we mean changes of rank – that is, departures
from fomal correspondence in which a unit at one rank in the SL is translated
into a unit at a different rank in the TL.
+ Intra-system shift: A system consists of a closed number of elements
among which a choice can be made. In fact, the terms available in each system
in one language can show fundamental differences from the terms of the same
system in another language. This can be considered as a major source of
shifts at this level of language description. In other words, intra-system shifts
refer to those changes that occur internally within a system. They are regarded
only on the assumption that formal correspondence between the two
languages, i.e. ST-TT should possess approximate systems. The equivalence is
said to occur at a non-corresponding term in the TL system. All languages have
their systems of number, deixis, articles, etc. Intra-system shifts occur when a
term is singular in the source text but its textual equivalent is plural in the TL,

10
or vice versa (e.g., a change in number even though the languages have the
same number system).
2.1.4. Translation equivalence
The concept of equivalence can be said to hold a central position in
translation studies, and many different theories of the concept of equivalence
have been elaborated within this field in the past fifty years.
Nida (1964) argues that there are two different types of equivalence,
namely formal equivalence, which is referred to as formal correspondence and
dynamic equivalence in the second edition by Nida and Taber (1982).
According to Nida and Taber (1982), formal correspondence 'focuses attention
on the message itself, in both form and content', while dynamic equivalence is
based upon 'the principle of equivalent effect' (p. 159). The two theorists
provide a more detailed explanation of each type of equivalence.
Formal correspondence occurs when a TL item represents the closest
equivalent of a SL word or phrase. Nida and Taber (1982) make it clear that
there are not always formal equivalents between language pairs. They, therefore,
suggest that these formal equivalents should be used wherever possible if the
translation aims at achieving formal rather than dynamic equivalence. Dynamic
equivalence is defined as a translation principle according to which a translator
seeks to translate the meaning of the original in such a way that the TL wording
will trigger the same impact on the TL audience as the original wording does
upon the ST audience. They argue that “frequently, the form of the original text
is changed; but as long as the change follows the rules of back transformation in
the source language, of contextual consistency in the transfer, and of
transformation in the receptor language, the message is preserved and the
translation is faithful” (Nida & Taber, 1982, p. 200)
Koller (1979) presents five types of equivalence as follows.

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1. Denotative equivalence: This orients towards the extralinguistic
conten transmitted by a text.
2. Connotative equivalence: This respect indicates that individual
expressions in the textual context have not only a denotative meaning but also
additional values which mean various or synonymous ways of expressions.
3. Text-normative equivalence: This has to do with text-type specific
features or text and language norms for given text types. To put it another way,
the SL and TL words are used in the same or similar context in their respective
language.
4. Pragmatic equivalence: This means translating the text for a particular
readership (i.e. the receiver to whom the translation is directed and to whom the
translation is tuned in order to achieve a given effect).
5. Formal equivalence: This aims to produce an analogy of form in the
translation by exploiting the formal possibilities of the TL or even by creating
new forms if necessary.
Baker (1992) approaches the concept of equivalence differently by
discussing the notion of non-equivalence at word level and above word level,
grammatical equivalence, textual equivalence, and pragmatic equivalence.
Non-equivalence at word level means that the target language has
no direct equivalent for a word which occurs in the source text. Common
problems of non-equivalence then involve such cases as culture-specific
concepts, the SL concept is not lexicalized in the target language, the SL word
is semantically complex, the SL and TL make different distinctions in meaning,
the TL lacks a superordinate, the TL lacks a specific term (hyponym),
differences in physical or interpersonal perspective, differences in
expressive meaning, difference in form, differences in frequency and
purpose of using specific forms, the use of loan words in the source text.

12
Non-equivalence above word level is closely related to the differences in
the collocational patterning of the SL and TL, which create potential pitfalls and
can pose various problems in translation.
Grammatical equivalence is more concerned with the differences in the
grammatical structures of the SL and TL, which often result in some change in
the information content of the message during the process of translation. This
change may take the form of adding to the target text information which is not
expressed in the source text. This can happen when the TL has a grammatical
category which the SL lacks. Likewise, the change in the information content of
the message may be in the form of omitting information specified in the
source text. If the TL lacks a grammatical category which exists in the SL, the
information expressed by that category may have to be ignored.
Textual equivalence is achieved through the realization of cohesion
or cohesive devices such as reference, substitution, ellipsis, and conjunction
and lexical cohesion from the source text into the target text.
Pragmatic equivalence is realized by means of studying and translating
coherence and implicature from the SL to the TL.
It is Baker’s ideas on the notion of equivalence that is of great importance
and interest to the study of this thesis since he points out most common
problems related to the issue and presents various strategies to deal with
them, which shed light on our investigation.
2.1.5. Translation errors
In this study, we observe the impact of machine translation errors in two
genres literary texts and technical text. There are many theories of many
researchers and this section below provide the definition of translation errors.

13
2.1.5.1. Definition of translation errors.
Mossop (1989) describes translation errors as “a given rendering will be
deemed to be non translation if it fails to conform to the concept of translation
predominant in the target culture” (p. 55). He identifies translation errors in
terms of cultural norm and formal equivalence. It means that the definition of
translation error by Mossop (1989) includes the achievement of formal
equivalence but excludes other critical factors such as smoothness, readability
and consistency in TL texts. Besides, formal equivalence, as defined by Nida
and Taber (1982), is a method of translating literally and protecting rhythm,
special stylistic forms, expression in syntax and lexis, metaphor, word play and
so on; therefore, formal equivalence is mainly used in translating poems and
songs, not all kinds of texts. A more thorough notion of error is proposed by
Pym (1992). This scholar supposes that translation errors may be attributed to
lack of comprehension; misuse of time; inappropriateness to readership,
language, pragmatics, culture; over-translation; under translation; discursive or
semantic inadequacy. Compared to the definition by Mossop (1989), Pym
(1992) suggests a large number of translation errors. However, these errors are
not systematically classified.
Ten years later, Aveling (2002) presents a more comprehensive and
systematic notion of translation errors. According to Aveling (2002), translation
errors occur when the translator fails to gain equivalence, adequacy and
accuracy. This definition is more comprehensive as it stresses that equivalence
covers many different types. Besides, it is more systematic because Aveling
(2002) emphasizes that translation errors can be divided into “dumb mistakes”
and “deliberate mistakes”. The former is due to the translator’s lack of
competence, and the latter occurs when the translator poses a purpose to
recreate the text.

14
2.1.5.2. Classification of translation errors.
Not only the definition but also the classification of translation errors has
attracted a myriad of efforts from scholars and researchers. Nevertheless, due to
the complexity of this practice, it remains intricate to establish a single
comprehensive list of all the translation errors observed.
House (1977) suggests that researchers should prepare separate profiles
for ST and TT; when the source text's and the translation text's profiles do not
match, there is an error. House describes two types of errors:
Covert errors: those which result from a mismatch of one situational
dimension with a similar one in TT, and covertly erroneous errors are
mismatches of the denotative meanings of ST and TT elements or a breach of
the target language system.
Overt errors: those which result from a non-dimensional mismatch. Such
errors can be divided into seven categories of:

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1. Not Translated
2. Slight Change in Meaning
3. Significant Change in Meaning
4. Distortion of Meaning
5. Breach of the SL System
6. Creative Translation
7. Cultural Filtering
The final stage in House's model is to list both covertly and overtly
erroneous errors and to make a statement of the relative match of the two
functional components.
If a translation error is defined as a failure to carry out the instructions
implied in the translation brief and as inadequate solutions to a translation
problem, then translation errors can be classified into four categories (Nord,
1997, p. 75).
Pragmatic translation errors are caused by inadequate solutions to
pragmatic translation problems such as a lack of receiver orientations.
Translation Errors
Covert Errors: result
from a mismatch of
one situational with a
similar one in TT.
Overt Errors: result
from a non-
dimensional mismatch,
and can be divided
into:

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Cultural translation errors are made by an inadequate decision with
regard to reproduction or adaptation of culture-specific conventions.
Linguistics translations errors are caused by an inadequate translation
when the focus is on language structures.
Text-specific translation errors, which are related to a text-specific
translation problem like the corresponding translation problems, can usually be
evaluated from a functional or pragmatic point of view.
As linguistic-based typologies tend to offer more information about the
types of errors found, the classification scheme employed in this study derives
from the aforementioned error typology by (Cabeceran.F et al., 2010). .

17
It has a hierarchical structure as shown in Figure 3. At the first level,
errors are split into five major categories: orthographic errors, morphological
errors, lexical errors, semantic errors, and syntactic errors.
Punctuation
orthographic errors Capitalization
Spelling
Verb
morphological errors Noun
Other
Errors Extra words
lexical errors
Missing words
semantic errors
Conjunction
syntactic errors Preposition
Article
Syntactic element
reordering
Category errors
This figure presents the hierarchical structure of the error typology. The
definition of each error type is provided in the following paragraphs:
An orthographic error occurs when a spelling error is found in the text
output. It is further distinguished into three subcategories: punctuation,
capitalization, and spelling. Punctuation errors are inappropriate usages of

18
marks and signs in a generated text. Capitalization errors are incorrect upper or
lower cases. Spelling errors occur when words are misspelled. This main
category excludes incorrectly inflected words, which are categorized as
morphological errors. The errors mentioned above induce only minor
disturbances for readers’ understanding of the target text.
The next category is morphological errors, which concern the form of a
TL word. It contains three subcategories: when a verb, a noun, or a word of any
other part-of-speech (POS) is of the wrong form. The first two subcategories do
not seriously affect the reader’s understanding of the generated text as a whole,
for English users can easily identify these errors and replace them with words of
the right forms. It is worth noting, though, that the impact of an error category
differs across target texts.
Some linguistic errors could have more influence than others on the
overall quality of the text outputs (Cabeceran.F et al., 2010). The last four
categories are more important than the first two in terms of their impact on
readers’ understanding of the target text. The first of them is lexical errors. One
broad definition of such errors is inappropriate lexical choices. For the
classification scheme employed in this study, the definition is narrowed down to
erroneous occurrences and non-occurrences of target words, i.e., extra words
and missing words. Extra word errors include any word that should be omitted
and cannot be substituted by another word of a more appropriate meaning; if the
erroneous word can be replaced by another word, it should be counted as a
semantic error. Missing words include only missing content words, for they
carry essential meanings for the text to be understood. Missing prepositions,
conjunctions, and articles are classified as syntactic errors, so that more
information of errors in sentence formation can be provided.
Semantic errors are found when the system chooses a wrong word to
render a source word. The meaning of this output word can be irrelevant as well

19
as related to that of its corresponding source word. For instance, it can be a
hypernym or hyponym of the source word.
Five subcategories are distinguished in syntactic errors, which concern
the structuring of sentence elements. Conjunction, preposition, and article errors
are all errors of the respective POS classes. Category errors occur when words
are rendered in wrong POS categories. In the English language, it is not easy for
a computer program to identify the POS or class of a word, as an English word
can sometimes be either a verb or a noun, depending on its context. The last
subcategory is of a more complicated notion. Syntactic element reordering
errors are when a sentence needs to be reordered or restructured. To avoid
confusion in the number of errors of this category, this error type is only
counted once per sentence.
2.1.6. Translation Quality Assessment (TQA).
The quality of a translation is a serious concern for Translation Quality
Assessment (TQA) approaches. The main issue is how to measure and express
this quality. There have been many attempts to find the way(s) to tackle these
issues and evaluate the quality of a TL text. However, it seems that among these
many approaches, only a few of them sound promising. One of the promising
approaches was the model provided by House (1996).
House's assessment model is based on Halliday’s Systemic-Functional
Theory (SFT), but it also draws eclectically on Prague School’s ideas, speech
act theory, pragmatics, discourse analysis and corpus-based distinctions
between the spoken and written language. It provides the means for the analysis
and comparison of an original text and its translation on three different levels:
Language/Text, Register (Field, Mode and Tenor) and Genre.

20
According to House (1977), the equivalent sought should be an
equivalent of function; that is, both source and TL texts must present the same
function and the text's function can only be made explicit through a detailed
analysis of the text itself.
This is the basis for the model, and what makes it different from other
criteria for establishing equivalence is the fact that those criteria rely either on
the writer's intention, an item that is not open to empirical investigation, or on
Individual Text’s
Functional Profile
Register
(Use of text)
Genre
(Type of text)
Field
What the text
is about.
What kinds
of things are
in the text?
Tenor
How the
author, the
reader, and
may be the
persons in the
text, relate to
each other
through the
text.
Mode
How the text
is
communicate
how its parts
fit together as
a text
A Specific Text
The words, and any non-
verbal content
Cultural Content
When and why the text
was composed?

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the reader's responses, which present problems to be measured. The function of
a text would then be "the application or use of what the text has in the particular
context of a situation" (House, 1997, p. 37).
Thus, each text is an individual text embedded in a unique situation, and
in order to characterize the text's function it is necessary to refer the text to the
situation. To accomplish this, the notion of situation has to be broken down into
the following specific situational dimensions (register) analysis: (House, 1997,
p. 45)
a. Field: refers to the subject matter and social action and covers the
specificity of lexical items.
Subject Matter: It can be a Novel, Poem, Play, etc.
Social Action: It can be Specific, General, Popular, etc.
b. Tenor:
'Tenor’ refers to who is taking part, to the nature of the participants, and to
the addresser and the addressee and the relationship between them. This
dimension includes the addresser’s temporal, geographical, social provenance as
well as his intellectual and emotional stance, i.e. his personal viewpoint vis-a-
vis the content he is portraying and the communicative task he is engaged in.
The ‘social’ role relationship’ may be either symmetrical (marked by the
existence of solidarity or equality) or asymmetrical (marked by the presence of
some kind of authority).
c. Mode:
In the scheme, “Mode” refers to both the channel – spoken or written
(which can be “simple”, e. g., “written to be read” or “complex”, e. g., “written
to be spoken as if not written”), and the degree to which potential or real
participation is allowed for between the interlocutors’ (House, 1997, p. 109).
Participation can also be either simple or complex. An example of simple
participation would be a monologue with no addressee-participation while
complex participation involves various addressee involving mechanisms

22
characterising the text, e.g. ‘a characteristic use of pronouns, switches between
declarative, imperative and interrogative sentence patterns or the presence of
contact parentheses, and exclamations’ (House, 1997, p. 40)
Medium: is Simple if it is written to be read and Complex if it is written
to be heard.
Participation: is Simple if it means monologue and Complex if it means
addressing a large community;
House defines ‘Genre’ as ‘a socially established category characterized in
terms of occurrence of use, source and a communicative purpose or any
combination of these’ (House, 1997, p. 107). When discussing the function of
the text, House uses the notions ‘ideational function’ (using language to
describe things in the external world and to present and evaluate arguments and
explanations) and ‘interpersonal function’ (using the language as an expression
of a speaker’s attitudes and his influence on the attitudes and behaviour of the
hearer).
2.2. Machine translation
Machine translation is the process of using software to translate text from
one natural language to another. During the last decade, the rapid development
of the Internet raised the interest in machine translation to overcome the barrier
of language.
2.2.1. The history/development of machine translation.
The term 'machine translation' (MT) refers to computerized systems
responsible for the production of translations with or without human assistance.
It excludes computer-based translation tools which support translators by
providing access to on-line dictionaries, remote terminology databanks,
transmission and reception of texts, etc. It was first developed in the 1950’s

23
as a computer system that performed automatic translation. In the
beginning, the system worked when the whole text in a source language (SL)
was translated into a target language (TL) as a single task without human
intervention. The source text output produced by machine translation is
known as ‘raw output’ as it provides a quick translation of the original.
These ‘raw outputs’ usually offer informative translation. It means, the output
produced by the machine translation only provides surface translation of the
target text without human involvement, as it is a statistical machine translation.
It is deemed to translate what is input into the system. Machine translation is
one of the oldest applications and has been used in computer science.
Nonetheless, due to the development of global needs for transferring knowledge
and information, it has been used in language and linguistic fields as well.
Additionally, the main objective of machine translation during the early stage
was to replace human translators as it was expected to do the translation
work. However, unsatisfactory output produced by the system and problems
that could not be solved due to lexical ambiguities produced by machine
translation made the enthusiasm among experts decline. Many companies that
developed machine translation at the early age started to admit that their
systems were not able to produce perfect translations. Therefore, due to the
failure system and unsatisfactory output produced, it has led to the development
of computer-assisted translation (CAT). Subsequently, computer-assisted
translation enables human intervention in the machinery system, thus, helping
translators to work quickly. It helps assist human translation’s work and gives a
human translator an extended control over the process.
2.2.2. Google Translate.
Machine translation is a sub-field of natural language processing and the
area of information technology. In general, it is based on computer technology
that uses software to translate one natural language into another. And, “Google

24
Translate” is an automatic machine-translation service provided by Google Inc.
It translates one written source language into another directly or with English as
a medium. It is a free translation service that currently provides instant
translations between 58 different languages. In addition, GT can translate words,
sentences and web pages between any combination of the supported languages.
GT has been created with the expectation to make useful information
universally accessible, regardless of the language in which it has been written.
(Google,2011). At the time, out of the three systems included in this study, GT
is the most extensive one as its range of supported languages is the greatest.
When GT generates a translation, it searches for patterns from hundreds of
millions of documents to help make a decision on the best available translation.
By identifying patterns in documents that have already been translated by
human translators, GT can make quick decisions as to what a suitable
translation could be. This procedure of seeking patterns in a large number of
texts is called Statistical Machine Translation (SMT), as presented in the
previous section. The more human-translated documents GT can analyze in a
specific language, the better the translation quality will be. This is why the
quality of a translation is likely to vary across languages (Google, 2011.) The
following figure presents all 58 languages currently supported by GT

25
Figure 1: Languages Supported by GT (Google 2011)
As seen in Figure 1, the variety of the languages supported by GT is
rather extensive. The so-called alpha languages are likely to have less reliable
translation quality than the other supported languages. However, Google is
trying to make them function better. Google has the intention of supporting
other languages as well, as soon as the translation quality is good enough.
Currently, the other free online MT systems are not able to compete with
Google with regard to the number of supported languages, giving it a
competitive advantage in the field of MT.
Translations produced by GT can be improved by selecting the wanted
alternative from the given alternative translations. For example, when the
translator encounters a translation that does not seem good enough, s/he can
simply click the phrase in question and choose a better option. By clicking the
option, GT will learn from the translator’s feedback and continue to improve
over time. In addition, the translator has the option of using Google Translator
Toolkit to upload translation memories online. When the translator logs in to

26
Google, the personally uploaded data will be taken into consideration while
translating documents. The next figure displays Google’s free online MT
system interface in its present form.
Figure 2: GT Graphical User Interface
Google’s GUI, as shown in Figure two, has been designed to look simple,
but it actually has surprisingly many features regardless of the plain design. The
ST box has been placed on the left, and the TT box on the right. Any text can be
just copied and pasted into the box. The SL and the TL can be selected, but in
case the user is uncertain of the SL, GT is able to automatically detect it. The
translation direction can be easily reversed by clicking on the reverse button. A
link of a website can also be pasted to the box, which will lead the user to the
posted site, but with a desired TL instead. Thus, the design of the webpage
remains untouched, but the language of the text changes. Translations can be
rated by the user according to three different categories: helpful, not helpful or
offensive. In addition, the word is highlighted in both texts when the mouse
cursor is moved onto a specific word. This makes it easier for the human
translator or the user to spot how GT has translated a particular word or
expression. With a recently added feature, by holding the shift key on the

27
keyboard, the user is able to drag and reorder words in the TT box. In addition,
the user can view alternate translations by clicking the translated words in the
TT box. The GT system also provides the user with a computer generated voice
which will read the texts out loud for those interested in listening to the texts.
Google’s translation software has been designed not only for the regular
computers but also for mobile devices. This has greatly expanded the
possibilities of using MT in different kinds of situations. A free downloadable
application of GT was programmed and released in August 2008 to utilize the
Iphone by Apple Inc. Additionally, GT was released in the Android Market for
smart mobile phones that use the Android operating system in January 2010.
The available mobile applications make Google’s services even more versatile
and competitive, reaching out to a greater number of users.
2.3. Genres
2.3.1. Definition of genre.
The word genre comes from the French (and originally Latin) word for
'kind' or 'class'. The term is widely used in rhetoric, literary theory, media theory,
and more recently linguistics, to refer to a distinctive type of 'text'. It is
described by Trosborg (1997, p. 6) as text category readily distinguished by
mature speakers of a language. According to Miller (1985, p. 151), a
rhetorically sound definition of genre must be centered not on the substance or
form of the discourse but on the action it is used to accomplish. Genre can be
recognized as a system for achieving social purposes by verbal means.
Therefore, for instance guidebooks, poems, business letters, and newspaper
articles can be referred to as genres because they are used in a particular
situation for a particular purpose. According to Longman Dictionary of
Contemporary English (2008), the word genre means a particular type of art,
writing, music, etc. which has certain features that all examples of this type
share. The term has a wide usage in rhetoric, media, theory, and even education

28
(especially linguistics) to refer to a special kind of text. For example, in art, we
are familiar with the genres of painting/drawing, sculpture and engraving. In
addition, within each genre, sub-genres have developed. For painting, sub-
genres might include landscape, portraiture, still life and non-representational
works. Some of the recognized sub-genres of fiction include novels, short
stories, and novellas. Presumably, any number of sub -levels can exist for any
one genre, and new sub-genres may be invented at any time. Recently, genre
theories have been promulgated for texts about every kind of human activity
(e.g., business, politics, medicine, religion, and sport, among others). In each,
genres and sub-genres can be identified.
2.3.2. Classification of genre.
Genre differs from topic, which is what a text is about. Theoretically, a
text from any given genre can be about any given topic (Finn and Kushmerick
2006), yet it is clear that co-variances exist between genre and topic, with some
genre–topic combinations more likely than others (cf. fiction vs. news reports
about dragons).
Because both genre classification and topic classification exploit low-
level features of text as a basis for their predictions, a feature indicative of topic
might benefit a genre classifier through correlations in the training corpus.
However, if the topics addressed in different genres can change unpredictably
over time, such correlated features can then harm performance. Although
domain adaptation techniques might remedy this, they typically require
extensive data in the target domain, and the remedy may fail as soon as the
distribution changes again. Many definitions of genre have been proposed so far
in literary studies, academic writing (e.g. professional settings and,
organizational environment, and so on. More specifically, in genre classification
studies, genres have often been seen from literary studies, where genres such
as novels, short stories, poems, plays etc have been studied for centuries.

29
CHAPTER 3. RESEARCH METHODS
This chapter elaborates on how the data will be collected and analyzed to
answer the research questions and achieve the research aim and research
objectives.
3.1. Research methods
Content analysis has a long history in research, dating back to the 18th
century in Scandinavia (Rosengren, 1981). A number of definitions of content
analysis are available. According to Berelson (1952) content analysis is a
research technique for the objective, systematic, and quantitative description of
the manifest content of the communication. Holsti (1968) says that it is any
technique for making inferences by systematically and objectively identifying
specified characteristics of messages. Kerlinger (1986) defined content analysis
as a method of studying and analyzing communication in a systematic, objective,
and quantitative the manner for the purpose of measuring variables. Content
analysis is a combination of qualitative and quantitative research, its an
intersection of qualitative and quantitative methods. Initially, researchers used
content analysis as either a qualitative or quantitative method in their studies
(Berelson, 1952). Later, content analysis was used primarily as a quantitative
research method, with text data coded into explicit categories and then
described using statistics.
In this study the researcher use quantitative content analysis to classifying,
analyzing the errors that will occur at the translational versions, interpreting
them and finally drawing a conclusion. In reality, the researcher uses descriptive
statistics and frequency tables to classifying and analyzing the data.

30
3.2. Data collection
This study aims to investigate the quality of Google Translate’s
translations of literary texts and technical texts from English into Vietnamese.
In order to achieve the above aim and objectives, the researcher will collect
data and analyze it in order to answer the following questions:
1. How does Google Translate translate literary texts and technical texts
in terms of translation quality?
2. Which of these two genres does Google Translate translate better? The
following steps will be followed when the data for the present study will be
collected and prepared for data analysis.
First of all, the researcher collects the technical texts is from
https://www.mdedge.com/internalmedicinenews. The texts include 2040 words.
And the literary texts is selected from
https://www.bartleby.com/ebook/adobe/3134.pdf.The story consists of 2340
words. After that, the researcher will draw a table in a Word file. The table has
two columns. The first column contains the original texts. The TL texts
corresponding to the original texts are typed in the second column. The errors in
the Vietnamese language are underlined so that the researcher could recognize
and compare them easily. The table is used for data analysis.
For instance:
English version Vietnamese version
Patients with the six deadliest
forms of cancer are five times less
likely to survive for five years or
more compared to patients with one
of 11 other forms of the disease, new
research has found.
Survival rates for pancreatic,
Bệnh nhân có sáu dạng ung thư
nguy hiểm nhất là 5 lần ít có khả năng
tồn tại trong 5 năm hoặc hơn so với
những bệnh nhân có một trong 11
dạng bệnh khác, nghiên cứu mới đã
tìm thấy.
Tỷ lệ sống sót đối với tuyến

31
liver, brain, lung, oesophageal and
stomach cancer are currently
“unacceptable”, according to a new
taskforce made up of five charities.
tụy, gan, não, phổi, thực quản và ung
thư dạ dày hiện nay là “không thể
chấp nhận”, theo một lực lượng đặc
nhiệm mới được tạo thành từ năm tổ
chức từ thiện.
In the first step, the researcher reads and analyzes each genre carefully to
choose sentences with errors occur. In the second step, the English sentences
and the corresponding Vietnamese sentences will be examined to see types of
errors in translation.
All of the Vietnamese sentences with errors will be listed in a table
described in 3.3. This table has three main columns named English sentences,
Vietnamese sentences, and type of errors. The first column consists of the
English text, and the second column consists of the Vietnamese text. The last
column is further split into six columns for six specific types of errors.
3.3. Data analysis
After all the literary texts, and technical texts were translated by GT and
typed in the table, the researcher added six other columns to the right of the
table for analysis. This study evaluates which genre of the two genres in
question GT produces the best TL text and which genre it produces the worst
TL text. Therefore, the classification of translation errors into six types will be
used as a conceptual framework for the analysis of the data.
The six types of errors include (1) orthographic errors, (2) capitalization
errors, (3) morphological errors, (4) lexical errors, (5) semantic errors, and (6)
syntactic errors.
First of all, the researcher uses the following codes in analyzing the data:
1. OE (orthographic errors)
2. CE (capitalization errors)

32
3. ME (morphological errors)
4. LE (lexical errors)
5. SE (semantic errors)
6. SYE (syntactic errors)
Secondly, the researcher carefully examines the TL texts to find out errors
and identify the type of each error. The result of the analysis of the data is a
table that looks like the following table.
Original text TL text Type of Errors
O
OE
C
CE
M
ME
L
LE
S
SE SYE
Patients with
the six deadliest
forms of cancer are
five times less likely
to survive for five
years or more
compared to patients
with one of 11 other
forms of the disease,
new research has
found.
Bệnh nhân có sáu
dạng ung thư nguy hiểm
nhất là 5 lần ít có khả
năng tồn tại trong 5 năm
hoặc hơn so với những
bệnh nhân có một trong
11 dạng bệnh khác,
nghiên cứu mới đã tìm
thấy.
x
Following this, the researcher starts counting the number of: (1) the
Vietnamese sentences which contain errors; (2) CE (capitalization errors) (3)
orthographic errors, (4) morphological errors, (5) lexical errors, (6) semantic
errors, (7) syntactic errors. The counting help the researcher figure out the
frequency of each type of errors in three genres and answer the question which

33
genre GT produces the best TL text and which genre Google Translate produces
the worst TL text.
First of all, each of the sentences in the literary texts, and technical texts
and their equivalents is compared individually to see whether the syntax,
structure, word, is matched between English and Vietnamese version. There
will be the sentences in the TL contain the errors when GT translated the SL
into the TL, and the researcher will investigate the sentence occurred the errors
when translating from SL to TL. The researcher investigates to see whether the
sentence in the TL errors in form, structure, grammar, and word, which kinds of
errors the GT has made.

34
CHAPTER 4. FINDINGS AND DISCUSSIONS
This chapter presents and discusses the results obtained from the findings
of the two texts (literary texts and technical texts). The findings were analyzed
and presented in tables according to the proposed research questions. The two
texts were translated using Google Translate and each of the texts was tabulated
according to the framework in chapter 2. In those 4000 word, in some cases,
one line has more than one error.
4.1 The frequency of errors in literal text and technician text
The chart above shows that this research aimed at examining the
translation errors in Vietnamese - to - English translation made by GT, through
the investigation the researcher realizes most of the errors occur in literal texts.
Based on the errors type explained in chapter II, there are six categories of
GT TRANSLATION
literal text
technical text

35
errors namely linguistic category, orthographic errors, capitalization errors,
morphological errors, lexical errors, semantic errors, syntactic errors
The results of the analysis of the types of equivalents are shown in the
table below.
TYPE OF ERRORS OCCURRENCES PERCENTAGE
orthographic errors 0 0%
morphological errors 689 32.53%
lexical errors 65 3.06%
semantic errors 311 14.68%
syntactic errors 1053 49.71%
Table 4.2: The frequency of errors occur in literary text and
technical text
As shown in Table 4.2, the findings of the Vietnamese-to-English
translation showed that the most frequent errors in 4000 words were
orthographic errors take the lowest percentage with 0%. And the highest is
syntactic errors with 1053 errors and 49.71 %. Next, the morphological errors
with 689 errors and 32.53%. The lexical errors and semantic errors takes 3.06%
and 14.68% .As clearly seen from the table, the most common errors GT made
was syntactic errors, while they rarely had problems with subject-verb
agreement, part of speech and capitalization.

36
Below are some examples of errors found in the translation version
Original text
Khi đọc truyện Gatsby vĩ đại của Scott Fitzgerald, tôi vô cùng thích thú
với đoạn mở đầu: "Hồi tôi còn nhỏ tuổi, nghĩa là hồi dễ bị nhiễm các thói hư tật
xấu hơn bây giờ, cha tôi có khuyên tôi một điều mà tôi ngẫm mãi cho đến nay:
Translational version
When I read Scott Fitzgerald's great Gatsby story, I was very interested in
the introduction: "When I was young, it meant that when I was more vulnerable
to bad habits than now, my father advised me one thing I think forever until
now:
As we can see, GT failed to translate the clause “nghĩa là hồi dễ bị nhiễm
các thói hư tật xấu hơn bây giờ” in Vietnamese, instead it was translated into “it
meant that when I was more vulnerable to bad habits than now”. It should have
been “when I was more susceptible to bad habits than now”. In the data,
Google Translate seems to follow the original structure by employing word-for-
word translation in translating instead of adapting the structure of TL. In this
sense, GT failed to adopt the context, in which the original text actually means
“influence the bad habit”.
Original text:
Điều đó luôn khiến tôi mỉm cười. Cuộc sống này cũng vậy... Ở đâu đó
ngoài kia là những người có thể giống ta. Ở đâu đó ngoài kia là những người có
thể rất khác ta.

37
Translational version:
That always makes me smile. This life is the same ... Somewhere out
there are people who may be like me. Somewhere out there are people who can
be very different.
Google Translate can translate words quite well but has difficulty
translating sentences, paragraphs, or complicated sentences. With slang words
or figurative meaning, Google Translate cannot handle it. In cases like this
sometimes Google Translate remains the original language. In case the user
uploads Vietnamese without accent, Google Translate will ... "give up".
This program translated from English to Vietnamese has many errors. For
example, the word “miễn bàn”, the tool will translate as “free table”. Or
translate from English to Vietnamese the program will translate some very
stupid sentences like “hello, how are you?”, It will translate into “Xin chào, làm
thế nào là bạn?”.
Similar to the above example, there is a word-for-word translation by GT
and it makes the readers hard to understand the context.
Punctuation 0 0
Spelling 0 0
Verb 282 19.23 %

38
Noun 212 14.46 %
Extra words 0 0
Missing words 0 0
Semantic errors 265 18.07%
Conjunction 222 15.14%
Article 242 16.50%
Syntactic element
reordering
111 11.14%
Category errors 132 13.24%
TOTAL 1466 100%
Table 4.3: The frequency of errors occur in literary text
As seen in Table 4.3 all types of errors occur, namely: Punctuation,
spelling, verb, noun, extra words, missing words, semantic errors, conjunction,
article, syntactic element reordering, category errors and translation in which
verb and semantic errors account for most of the errors committed by the GT
with the frequencies of 282 errors (19.23%) and 265 errors (18.07%),
respectively. And 222 occurrences are conjunction, accounting for 15.14% of all
the errors. The article, syntactic element reordering and category errors take 242
errors (16.50%), and 111 errors (11.14%) finally 132 errors (13.24%). The
lowest frequency belongs to punctuation, spelling, extra words, missing words
with 0%.

39
Punctuation 0 0
Spelling 0 0
Verb 195 29.9%
Noun 0 0
Extra words 0 0
Missing words 65 9.9%
Semantic errors 46 7%
Conjunction 0 0
Article 135 20.7%
Syntactic element
reordering
85 13.03%
Category errors 126 19.32%
TOTAL 652 100%
Table 4.4: The frequency of errors occur in technical text
Table 4.4 shows the distribution of errors relating to the translation of
technician texts. Overall, the verb and article and category errors take the
highest proportion with 195 errors (29.9%) and 135 errors (20.7%) and 126
errors (19.32). The syntactic element reordering takes 85 errors with 13.03%
and missing words takes 46 errors with 7%. The lowest proportion with
punctuation, spelling, noun, extra words, conjunction with 0%.

40
4.2. Article
Since the concept of article is not so evident in Vietnamese, the
participants almost always ignored its importance in the given English texts. In
fact, the English article plays a crucial role in identifying nouns and its
appearance or non-appearance in a sentence produces different meaning. The
study implied that because the GT were not sensitive to article, the errors
readily occurred. Its frequency was 242 in literal text and from their translations,
the mistakes could be developmental errors as the participants did not have
enough knowledge of the article use (here including zero article, too) especially
in certain expressions. For instance, in office was translated as in the office.
Original text
Vì vậy tôi không thích bình phẩm một ai hết. Lối sống ấy đã mở ra cho
tôi thấy nhiều bản tính kì quặc, nhưng đồng thời khiến tôi trở thành nạn nhân
của không ít kẻ chuyên quấy rầy người khác.
So I don't like to comment on anyone. That way of life opened me up to
many oddities, but at the same time made me victim of many people who
disturbed others.
For instance, the proper noun the victim, was translated by GT without
the, and this version should be: “That way of life opened me up to many
oddities, but at the same time made me the victim of many people who
disturbed others”.

41
4.3. Pronoun
The high frequency of pronoun errors in translations to English is striking.
These errors are significant because many of the errors involve basic personal
pronouns (I, you, he, she, it, etc.). Consequently, the errors do not occur because
the translation system has encountered rare or out-of-domain language. Instead,
the errors are caused by significant linguistic differences between the two
languages and by the fact that use of pronouns depends on their context.
Another difference between English and Vietnamese can cause incorrect
pronouns to occur in machine translations. The Vietnamese has the property of
gender associated with nouns. Every noun is either masculine or feminine, and
there are no pronouns like it. Consequently, in order to select the correct
English pronoun form, it is necessary to know the referent of the pronoun. Some
examples of this problem are provided in.
Original text
Sao ta phải lấy làm lạ về điều đó? Sao ta phải bực mình về điều đó? Sao
ta lại muốn rằng tất cả mọi người đều phải nhảy lên khi nhìn thấy thác Niagara?
Why do we have to wonder about that? Why we must upset about that?
Why do we want everyone to jump up when they see Niagara Falls?
In this example the subject in original version is singular noun however in
translational version is plural noun. In this example illustrates another
difference between English and Vietnam that produces problems for machine

42
translation. Vietnamese is a language in which subject pronouns are not usually
expressed.
Consequently, the inflection on the verb can be adequate to indicate
which pronominal subject the speaker intends, and in all three languages, the
subject pronoun is usually produced only for emphasis. In contrast, English
requires the subject pronoun to be expressed even when the verb form would
permit no other subject.
This type of error in the Vietnamese-to-English translation emphasized
that the participants did not apprehend English tenses precisely. Their patterns
of errors were seen as an incorrect verb form, subject-verb agreement, and tense
selection. For instance, most of them translated Vietnamese past tense into
English present tense; present perfect to present simple or past tense.
4.4. Google improvements
Google has launched a new campaign that allows users to directly
contribute to improving the quality of translations on Google Translate with
very simple operations.
To use the application, users can perform the following steps:
Step 1: Users log into Google account and access Google Translate tool
Step 2: Click on Help us improve Google Translate. Google will introduce
4 new features to help users improve translation quality on google, including:
- Translate: Translate words or phrases.
- Match: Match the words with the appropriate translation.

43
- Rate: Evaluate the quality of the translation.
- Validate: Check the quality of the translations.
Currently, with the Vietnamese, Google Translate only supports 2 features:
Translate and Validate.
Click on Got it to continue.
Step 3: Click on My languages to select the languages of the translations.
Users need to select at least 2 languages to compare and can select up to 5
languages.
Step 4: Users can choose to translate with the Translate feature or check
the translations with the Validate feature.
- For Translation: Google Translate will provide completely random
words or phrases. After entering the translation plan into the tool, users choose
Submit to continue with another word. For meaningless words or no answer
plan, user select Skip to skip.
- For Validate: Google Translate will provide translation options
corresponding to a given word. The user chooses the right and wrong option by
checking the boxes of the two Correct and Incorrect columns. Once completed,
the user selects Submit to continue. Users can skip by selecting Skip to come to
the next word.
Step 5: Click My Answers in the left column to check the number of
answers (or scores) users have made on Google Translate. For those with high

44
scores in the Top 5 within the country, Google will donate special certificates
and gifts from the company.

45
CHAPTER 5. CONCLUSIONS AND IMPLICATIONS
In this final chapter, I will pull together the threads from the previous
sections. And, I briefly reflect on the meaning of this thesis.
5.1 Summary of the findings
The evaluation conducted on the literal text and technical text shows that
Google Translate is not successful in producing outputs which are fully
comprehensible to the target reader. This study aimed to investigate translation
errors on language structure and meaning made by GT. In doing so, the
frequency and percentage of literary text and technical text were figured out.
The errors in literal text Vietnam to English translation were verb 282 errors
(19.23%) and semantic errors 265 errors (18.07%), respectively. And 222
occurrences are conjunction, accounting for 15.14% of all the errors. syntactic
element reordering and category errors take 242 errors (16.50%), and 111 errors
(11.14%) finally 132 errors (13.24%). The lowest frequency belongs to
punctuation, spelling, extra words, missing words with 0%.
In technical text, the most to the least found with verb 195 errors (29.9%)
and article 135 errors (20.7%) and category errors 126 errors (19.32). The
syntactic element reordering takes 85 errors with 13.03% and missing words
takes 46 errors with 7%. The lowest proportion with punctuation, spelling, noun,
extra words, conjunction with 0%.

46
5.2 Implications for translation
Due to the fact that this study examined the errors of literal text and
technical text were translated from Vietnamese to English made by GT. Firstly,
the results of the study can be used as a guideline for teachers to recognize the
errors found in this study e.g. punctuation, spelling, verb, noun, extra words,
missing words, semantic errors, conjunction, article, syntactic element
reordering, category errors, and find appropriate solutions for these errors.
Secondly, the results can indicate the strengths and weaknesses of the GT as
well as provide the ways to improve them effectively. The results implied that
the GT had the most difficulties in translating literal text. These findings are
beneficial to teachers in that they should give more emphasis on these error
types. Finally, the school or authority can use these results to design the
curriculum in order to enhance their English competence as well as set an
appropriate and supportive environment for their language learning. In
particular, the subjects of English semantics and collocation should be taught.
The students should expose to the English culture more by speaking with native
speakers and studying authentic English texts, for instance.
5.3. Limitations of the study
Although the study achieved positive results, there are some limitations as
follows:
Firstly, the paper is limited to 4,000 words of the lesson, so there may be errors
encountered in other specialties that are not yet statistics.

47
Secondly, research time is another limitation. Because of the limited time of
research and knowledge, the research has many flaws that are not really perfect
5.4. Suggestions for further studies
From the results and the limitations of the study, some suggestions for further
research are made as follows:
Firstly, the results found on the translation models by GT cannot be generalized
to all majors. Therefore, it is proposed that future studies will be carried out with a
wider target population for more evidence of quality of translation results in GT.
Secondly, further studies are proposed to improve the translation feature of GT
more accurately.

48
REFERENCES
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(2010). Linguistic-based evaluation criteria to identify statistical machine
translation errors. Paper presented at the The 14th Annual Conference of
the European Association for Machine Translation, Saint-Raphaël.
Catford, J. C. (1965). A linguistic theory of translation. Oxford: Oxford
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Hatim, B., & Mason, I. (1990). Discourse and the translator. London: Longman.
Holsti, O. R. (1968). Content analysis. In G. L. E. A. (Eds.) (Ed.), The
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Newmark, P. (1985). About translation. Toronto, Canada: Multilingual Matters
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Radford, A. (1997). Syntactic theory and the structure of English: A minimalist
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Trosborg, A. (1997). Text typology: Register, genre and text type.
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