error analysis of rule-based machine translation outputs a case study on english – persian mt...
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Error Analysis of Rule-based Machine
Translation Outputs
A Case Study on English – Persian MT
System
تجزیه و تحلیل خطا از حکومت مبتنی بر خروجی دستگاه ترجمه؟
مطالعه موردی در زبان انگلیسی - فارسی MTسیستم های
Zahra PourniksefatIslamic Azad University – Science & Research
Branch
Agenda
Introduction Machine Translation Overview Evaluation of MT systems
Methods & Materials Error Categories & DescriptionResults & Discussion
Machine Translation Overview
Definition: The term Machine Translation (MT) is used for translating text or
speech from one natural language to another by using computers and software.
• Systran: MT is much faster than human translators because it is much
cheaper and has a better memory than human translators.
• Shahba 2002 believed that “It’s better to spend our time on the actual act of
translation rather than typing the English text or scanning it for the MT to
translate. Efforts in MT are by themselves valuable as they at least satisfy one of
the needs of human beings: need for innovation and discovery”
• MT is more economic on time and money, but it is less accurate than human
translators (Frederking, 2004).
Why MT matters?
According to Hatim and Munday it’s an important topic - socially, politically,
commercially, scientifically, and intellectually or philosophically (2004)
• The social or political importance of MT arises from the socio- political importance of translation
in communities where more than one language is generally spoken. So translation is necessary for
communication- for ordinary human interaction, and for gathering the information one needs to
play a full part in society.
• The commercial importance of MT is a result of related factors. First, translation itself is
commercially important. Second, translation is expensive.
• Scientifically, MT is interesting, because it is an obvious application and testing ground for many
ideas in Computer Science, Artificial Intelligence, and Linguistics.
• Philosophically , MT is interesting, because it represents an attempt to automate an activity that
can require the full range of human knowledge.
Some Misconceptions about MT
MT is a waste of time because you will never make a machine that can translate
Shakespeare. This criticism that MT systems cannot translate Shakespeare is a bit like
the criticism of industrial robots for not being able to dance.(Hatim and Munday, 2004)
• First, translating literature requires special literary skills – it is not the kind of
thing that the average professional translators normally attempt
• Second, literary translation is a small proportion of the translation that has to
be done.
• Finally, one may wonder who would ever want to translate Shakespeare by
machine – it’s a job that human translators find challenging and rewarding, and
it’s not a job that MT systems have been designed for.
Approaches to MT• Direct Machine Translation Approach
The first developed MT systems where a word–for–word translation from the source language to the target
language is performed.
• Transfer Machine Translation Approach
1. The analysis stage that is the direct strategy which takes benefits of a dictionary in source language to
demonstrate the source language from linguistic point of view.
2. The transfer stage varies the outputs of the analysis stage to produce structural and linguistic equivalents
between the two languages.
3. The generation stage is the third stage in which a target language dictionary is applied to result the target
language document on the basis of linguistic information. (Steiner, 1988)
• Interlingua Machine Translation Approach
First the source text meaning is decoded
Second the resulted meaning is re-encoded in the target language
Approaches to MT cont’d.• Rule-based Machine Translation Approach
It operates on the linguistic data on source and target languages fundamentally
taken from bilingual dictionaries and the basic semantic, morphological, and
syntactic grammar of the individual language (Gelbukh, 2011).
Minimally, to get a Persian translation of English sentence one needs:
1. A dictionary that will map each English word to an appropriate Persian word.
2. Rules representing regular English sentence structure
3. Rules representing regular Persian sentence structure
4. And finally, we need rules according to which one can relate these two
structures together.
Approaches to MT cont’d.
• Statistical Machine Translation Approach
This system uses a corpus or database as a translated example for analyzing and decoding source
language. In comparison with the machine translation of about three decades ago, Google Translate
as an example of more contemporary automated engine for the task of translation has taken a giant
leap. However, it is still too imperfect. (Nierenberg, 1998)
• Hybrid Machine Translation Approach
1. Rules post-processed by statistics in which translation are practiced on the pivot of rule-
based engine. Next statistics are applied to correct the output.
2. Statistics guided by rules in which rules have an important role to pre-process date to
quite the statistical representation to normalize. This approach is powerful, flexible and
under more control when it’s translating.
Evaluation of MT Systems• Human translation assessment (Secară 2005; Williams 2001) has been
moving from microtextual, word- or sentence-level error analysis methods
toward more macrotextual methods focused on the function, purpose and
effect of the text. At the same time, machine translation assessment has
mainly been microtextual and focused on the aspects of accuracy and
fluency.
• Hovy (2002) discussed the complexity of MT evaluation, and stressed the
importance of adjusting evaluation to the purpose and context of the
translation.
Evaluation of MT Systems cont’d.
Mary A. Flangan Believed that Machine translation quality can be difficult to quantify for a
number of reasons:
1) A text can have several different translations, all of which are correct.
2) Defining the boundaries of errors in MT output is often difficult. Errors sometimes involve only single
words, but more often involve phrases, discontinuous expressions, word order or relationships
across sentence boundaries. Therefore, simply counting the number of wrong words in the translation is not
meaningful.
3) One error can lead to another. For example, if the part of speech of a word is identified incorrectly by the
MT software, the entire analysis of the sentence may be affected, creating a chain of errors.
4) The cause of errors in MT output is not always apparent. The evaluator usually does not have access to a
trace of the software's tests and actions. Thus it can be difficult to identify what went wrong in the
translation of a sentence.
Evaluation of MT Systems cont’d.
Types of Evaluation Automatic Evaluation
the Word Error Rate (WER), the Position independent word Error Rate (PER), the
BLEU (Papineni et al., 2002) and the NIST (Doddington, 2002) where the MT
output is compared to one or more human reference translations.
Human Evaluation
Due to the complexity of natural language, manual evaluation seems more reliable
1. Three passages were selected and translated by Rule-based MT Systems
and compared with one Statistical MT System and Human translator
2. Error categories were derived after the analysis of each text
Methods & Materials • Three passages were translated by two different MT systems and also a human
translator. • From each text type a passage of approximately 400 words was taken from story,
user guide and magazine.• The rule-based MT – Arya TM– system was designed based on thousands of lexical
and grammatical rules.• The statistical system, Google Translate by Google Inc., is based on the use of large
monolingual and parallel corpora for translation. • The unit of analysis was set to a sentence level because it’s the largest unit which
can be easily recognized in MT systems and ST sentence can be clearly corresponded to its TT pairs.
Table of Source Text Passages for Analysis Number of Words Number of Sentences
Short Story The Lottery
398
13
User GuideMicrosoft Access 2012
390
16
Magazine Academic article
415
15
Errors Category
Syntactic
Word Order
Missing
Words
Punctuation
Parts of
Speech Conjugation
Unknown Words
Semantic
Incorrect Words Polysemy
Idiomatic Expressions
• For English-to-Persian Rule- based MT systems the following categories were
derived
Error Categories & Descriptions
Error Categories & Descriptions cont’d.
Description of Error Categories:
• Syntactic Errors: Those errors that are related to the grammar of the language such as
parts of speech or conjugation
Word order that means sentence elements ordered incorrectly
Example: Commands generally take the form of buttons and lists. (User Guide)
Missing words: incorrect elision of some words
Example: This requires better data collection and analysis tools for studying outcomes and
consistent use of these tools across individual studies. (Magazine)
Arya Translation System و گیرد می شاگرد فرم کلی بطور ها دستور
ها . فهرست
Google Translate . لیست و ها دکمه شکل به کلی طور به دستورات
Arya Translation System ها ابزار و های نیاز بهتر اطالعات مجموعه این
سازگار و ها حاصل کن می مطالعه برای تحلیل
کند . می استفاده
Google Translate ابزار از استفاده با و بهتر ها داده آوری جمع مستلزم امر این
این از مداوم استفاده و نتایج بررسی برای تحلیل و تجزیه
. فردی مطالعات سراسر در ابزار
Error Categories & Descriptions cont’d.
Unknown words: word not in a dictionary
Example: The women, wearing faded house dresses and sweaters, came shortly after their
menfolk.( Story)
Punctuation: incorrect punctuation Example: The children assembled first, of course. (Story)
Arya Translation System محو , ها ژاکت و خانه ها لباس کننده خسته ها زن
از پس زودی به ، آمدند menfolkکردند شان
Google Translate مدت آمد در خانه، پژمرده ژاکت و لباس پوشیدن زنان،
از پس .menfolkکوتاهی را خود
Arya Translations کردند , جمع البته اول ها . بچه
Google Translations البته اول، مونتاژ .کودکان
Error Categories & Descriptions cont’d.
Parts of speech: errors in identifying pars of speech such as noun or verb
Example: If you decrease the width of the ribbon, small button labels disappear. (User Guide)
Conjugation: incorrectly formed verb or wrong tense
Example: Soon the women, standing by their husbands, began to call to their children, and the
children came reluctantly, having to be called four or five times.
Arya Translation System ناپدید کوچک دکمه ، کاهشبیابید نوار پهنا شما اگر
زند می برچسب
Google Translate کوچک دکمه ها برچسب دهد، کاهش را شما نوار عرض اگر
شوند می ناپدید
Arya Translations شروع , , به شان های شوهر کن می حمایت ها زن بزودی
می , ، آمدند اکراه با ها بچه و شان ها بچه به صدا کردن
دوره . پنج یا چهار زده صدا اشد که دارد
Google Translations فرزندان به تماس به شروع ایستاده، خود شوهران زنان، زودی به
. بار پنج یا چهار نام به اکراه، به ها بچه و خود،
Error Categories & Descriptions cont’d.
• Semantic Errors: Those errors that are related to the meaning such as incorrect meaning
of words or expressions which caused the incorrect meaning of the whole sentence.
Incorrect word: completely incorrect meaning
Polysemy: incorrect selection of the meaning of the words with more than one meaning
Example: The people of the village began to gather in the square, between the post office and the bank,
around ten o'clock.
Style and idiomatic expression : incorrect translation of multi-word expression
Example: They greeted one another and exchanged bits of gossip as they went to join their husbands. Arya Translations کردند معاوضه غیبت ذره و همدیگر سالم آنها
شان . های شوهر کنند وصل که رفتند آنها
Google Translations بی شایعات از بیت بدل و رد و یکدیگر استقبال آنها
خود شوهر به پیوستن برای را آنها عنوان به را اساس
رفت.
Arya Translations جمع که کردند شروع روستا مردم
و , پستخانه میان در مربع در شوند
ساعت دsه حدود ، بانکGoogle Translations آوری، جمع به شروع میدان در روستا مردم
ده حدود ساعت بانک، و پست اداره .بین
Results & Discussions
RBMT
SMT
Human
Word Order Missing Words Unknown Words
Punctuation Parts of Speech Conjugation
Story 12 6 3 12 8 9
User Guide 17 5 1 10 5 13
Magazine 14 4 10 7 15
Story 11 7 3 12 7 8
User Guide 17 5 1 9 7 11
Magazine 15 2 5 9 6 14
Story 1 2 0 3 1 3
User Guide 0 0 2 1 0 1
Magazine 2 1 1 1 2 2
Syntactic Category
Word Order Missing Words
Unknown Words Punctuation Parts of
Speech Conjugation
Tabl
e of
Syn
tact
ic E
rror
s
RBMT SMT Human
Incorrect Lexicon Polysemy Idiomatic Expression
Story 10 7 12
User Guide 7 9 5
Magazine 7 11 9
Story 8 8 9
User Guide 3 7 3
Magazine 5 13 8
Story 0 0 2
User Guide 1 0 0
Magazine 0 1 1
Semantic Category
Incorrect Lexicon Polysemy Idiomatic Expression
Tabl
e of
Sem
anti
c E
rror
s
Results & Discussions contd.
Results & Discussions cont’d.
• Both systems made the least errors with the simpler sentences and the most ones with the
compound- complex sentences, as well as lexically or structurally ambiguous texts. This is because
ambiguous source texts with different contents can correspond with more than one representation.
• For the rule-based system, the most typical errors are in conjugation, word order and also in
rendering polysemous words and idiomatic expressions. For the statistical system the most
common error is in conjugating and determining the tense. However, it has also some problems in
translating words with multiple meaning and idiomatic expression.
• To see whether machine translation accuracy is affected by text-type three different genres were
analyzed thoroughly. And for the different text types, the rule- based system had similar amounts
of syntactic and semantic errors in each text.
Future!
• Evaluating MT quality is necessarily a subjective process because it involves
human judgments.
• Determining the best category for an error in MT output is not easy because
we have to place them on how they are realized rather than the cause of errors
and many machine translated sentences contained multiple, linked errors.
• Future work will therefore be focused on the cause of errors and ranking error
categories. The error categories presented here is flexible, allowing for the
deletion or addition of more categories.