Computer-Aided Translation
Technology: A Practical
Introduction
Lingua Inglese
Università degli Studi di Roma Tor Vergata
17 pag.
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
INTRODUCTION
CAT technology can be understood to include any type of computeryzed tool that translators
use to help them do their job. This could encompass tools such as word processors, grammar
checkers, e-mail, and the World Wide Web (WWW). While these are certainly valuable,
possibly even indispensable, tools for a modern day translator.
1. WHY DO TRANSLATORS NEED TO LEARN ABOUT TECHNOLOGY?
Kingscott and Haynes both issue warnings that the pace of change is beginning to
accellerate. They foresee a dramatic increase in the use of CAT tools and note that this
increase will be needs.driven, rather than research-driven. What does it mean? In a global
marketplace, companies are finding out that failure to translate results in a loss of
international sales. A clear example of this trend can be found in the software localization
industry. The term “localization” refers to the process of costumizing or adapting a product
for a target language and culture. Thibodeau (2000) noticed that American software
companies often report international revenues exceeding 50% of total sales. The main
reason for localizing a product is economic. But why? A product that is not making profit
in the domestic market may perform better in another market. A non-localized product is
less likely to survive over the long run localization can extend a product’s life cycle. Most
products can be more profitable overseas because these markets often supports higher
prices. In order to stay competitive and increase profits, companies in a variety of fields
localize their products and web sites. Many companies now aim at launching a product or a
web site and its accompanying document at the same time,
a practice known as simultaneous shipping, or “simship”. Localization results in higher
translation costs and a slower time-to-market; and time is money. Therefore the translator is
sometimes required to work very quickly (and cheaply).
Shaler feels that by the time they graduate, translation students must be aware of the wide
variety of translation tools available and have had some exposure to a representative
selection of these tools. In addition, they should have learned about the financial and
operational implications of the introduction and use of translation tools in a traditional
translation environment.
Shaler and Kingscott are not the only ones who feel that technology deserves a more
prominent place in the translation curriculum.
Austermühl (2001): familiarity with CAT technology is becoming a prerequisite for
professional translators. These skills are in great demand in the translation marketplace.
Kenny (1999): graduates who are conversant in CAT technology are a real advantage when
it comes to working in highly technologized translation environment, such as the software
industry and the organizations of the European Union.
But what impact did CAT technology have on the need for translation professionals? Brooks
(2000): the creation of the printing press eliminated the job of the scribe, but it created a
larger market for books greater demand for authors, editors and illustrators. By analogy,
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
despite what someone might think, the development of CAT tools has not reduced the
needfor language professional. On the contrary, it has created jobs for translators who are
skilled at using technology!
1.1 Exploring the impact of technology on translation pedagogy
Bowker (2002). Additional benefits to be gained by introducing translation technology into
the translator-training curriculum: Exploring the impact of technology on translation
pedagogy:
■
Investigating human-machine interaction
■
Learning to evaluate technology
■
Examining how tools can change conventional practices
■
Producing data for empirical investigations
■
Reinforcing basic translation skills
Integrating technology into the translation curriculum can have an impact on the way in
which translation itself is taught.
Scherf (1992): the use of technology has led to an individualization of the teaching process.
Students can work at their own pace, while trainers have the opportunity to watch them in
the translation process then they can have class-wide discussions
Ahrenberg and Merkel (1996): the use of tools such as TM forces students to contemplate
issues such as text type and consider the intra- and inter-textual features of texts.
1.2.
Investigating human-machine interaction
By reflecting on the nature of machines and how they work, students can learn to adjust
their expectations about what machines can and cannot do. They can learn to adapt their
working practices to maximise the benefits to be gained from using CAT tools. As students
use technology, they become more aware of the fact that the computers are not capable of
applying intelligence and common sense to a task in the same way that humans would.
1.3.
Learning to evaluate technology
As students learn to use a particular tool, they should also be encouraged to evaluate that
tool in terms of its potential for helping them to complete their task more efficiently.
Students can also learn to assess tools in the light of a particular task or project in order to
determine which type of tool can best help them carry out that task
1.1.4 Examining how tools can change conventional practices
It has been observed that technology can sometimes change the very nature of the task that
it was designed to facilitate.
EXAMPLES
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
When recording information on term records, translators no longer record just the base form
of the term. Instead, they record multiple forms of the term so that they can minimize
editing by cutting and pasting the appropriate form directly into the target text .
Translators who use TMS tend to formulate their texts in such a way
as to maximise their potential for reuse.
1.1.5 Producing data for empirical investigations
A by-product of the use of CAT technology is the gradual accumulation of data that can be
used for other types of studies
Electronic corpora and TMs can provide large quantities of easily accessible data that can be
used to study translation:
■
Bilingual parallel corpora can be used to investigate translation strategies and
decisions.
■
Trainers can build an archive of students translations,
which can be used to guide teaching practices.
1.1.6 Reinforcing basic translation skills
Time spent teaching technology does not necessarily take away from time spent on other
translation skills. If technology is properly integrated into the translation classroom, it not
only allows the students to develop new skills, it also leads to an intensification of the basic
translation curriculum.
L’Homme (1999): students who use technology to produce term records, find solutions in
parallel documentation, or produce actual translations are reinforcing these basic translation
skills as well as developing good and realistic working practices that can later be applied in
the workplace.
2. CAPTURING DATA IN ELECTRONIC FORM
Barnbook (1996): Before you can analyse a text it needs to be in a format in which the
computer can recognise it, usually in the format of a standard text file on a storage medium.
In order for a translator to take advantage of specialized translation technology, the texts to
be processed must be in electronic form. In any case, when data are not currently machinereadable document arrives as a fax or a prontout, they must be converted. Ther are two main
ways of doing this: 1. Using a combination o scanning hardware and optical-characterrecognition software; 2. Using voice-recognition technology.
Optical-character recognition: OCR software takes the scanned image and, through a
process of pattern matching, converts the stored image of the text into a form that is truly
machine-readable and can be processed by other software. At its most basic, OCR software
examine each character in the scanned image and compares it to a series of character
patterns stored in a database. Once all the characters have been processed in this way,
the new file can be saved in an appropriate format (e.g. a text file) and opened in an
application such as a word processor, where it can be edited.
A scanner is a computer peripheral, there are several type of scanner:
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
•
Handheld scanner are small and lightweight, and have a limited scanning window.
Inexpensive.
•
Large freestanding units are capable of processing vast amounts of data quickly or
accurately but they are quite large and relatively expensive.
•
Flatbed scanner that looks like a small photocopier, this is the type of scanner that is
best suited to the needs of most translators. It is easy to use and can process
reasonable amounts of texts fairly quickly and accurately.
Once the document is in position, the scanner draws an imaginary grid over the document,
dividing it up into tiny segments known as pixels. The greater the number of pixels, the
higher the resolution of the scanner.
Factors affecting the accuracy of OCR
2.1.
•
Quality of the hard copy
•
Layout of the text
•
Quality of the scanning device
•
…
Typical mistakes: number “5” mistaken for letter “s”, letter “r” and “n” mistakenly
combined to form letter “m”, letters “c” and “l” mistakenly combined to form letter “d”.
In such circumstances, the OCR software does not pick out all the characters.
A technique is to integrate a dictionary checking stage, but that way don’t solve all
misinterpretation problems. If an OCR program mistakenly identifies the “e” in read as an
“o”, the resulting word will be “road”, which will not be identified as an error during a
dictionary check because it is a legitimate word. In future OCR software may need to take
even larger contexts into account in order to determine whether a given word makes sense in
this larger context.
2.1.
Voice recognition
Voice recognition, also known as speech recognition, is a technology that allows a user to
interact with a computer by speaking to it instead of using a keyboard or a mouse. The user
speaks into a microphone linked to a computer. The software acoustically analyses the
speech input by breaking down the sounds that the hardware “hears” into smaller,
indivisible sounds called phonemes. Then a “best guess” algorithm is used to map the
phonemes and syllables into words. The computer’s guesses are then compared against a
database of stores word patterns.
Voice recognition also uses grammatical context and frequency to predict possible words.
These statistic tools reduce the amount of time it takes the software to search through the
database but also help differentiate between homophones – words that sound the same but
are spelled differently and have different meanings, such as “to”, “too”, or “two”.
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
EXAMPLE: in a context such as the following word being “many”, it may seems a logical
“best guess” that the preceding word is “too”, “There are too many people in this room”.
Type of word recognition:
•
A voice-recognition dictation system that is speaker dependent requires a user to
“train” the software before it can be used. Training consists of reading through a
manufacturer-provided list of word or sentences that contain all the different sounds
in a language. The use is required to pronounce each word on the list several times
before moving on to the next word. A system that has been trained by user A cannot
then be used successfully by user B.
•
Speaker-indipendent voice-recognition technology, also known as universal voice
recognition, does not require user training. The manufaturers have pre-trained the
software using speech samples from a wide range of people. But if the user speaks
with a foreign accent it may be diffucult to get the system to work properly.
Another distinction between voice-recognition system is whether they are command/control
system or dictation system.
•
Command/control system allow users to interact with the computer by giving a
limited set of commands, which usually correspond to the commands found on
application menus (open, copy, paste ecc.)
•
Dictation system allow users to enter new data into the computer by dictating insted
of typing
PROS:
•
Good for poor typists
•
Good for those who suffer from a physical or visual impairment
•
Dictating is normally quicker than typing
CONS:
•
It takes time to edit the text and correct errors made by the VR
•
VR systems work for specific languages (= money for multiple packages)
3. Corpora and Corpus-analysis tools
“Putting a word in context means breathing life into it […] If you want to know how
words behave you must study them in their natural environment, and the natural
environment of words is text, context” (Roumen & Van der Ster, 1993, in Bowker,
2002).
The word “corpus” comes from Latin. […] its sense of “body of a person” started in the
mid-fifteenth century and the sense of “collection of facts or things” occurred later in
1727. The year 1956 saw an extension of the meaning to include “the body of written or
spoken material upon which a linguistic analysis is based”.
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
Leech (1992): Corpora of text collection had been used by linguists and grammarians for
the study of language long before the invention of the computer.
“computer corpus linguistics” (and not only “corpus linguistics”) would be a more
appropriate term for studies based on language database today.
Baker (1995): “Corpus-based translation study” (CTS) is the use of corpus linguistic
technologies to inform and elucidate the translation process.
What is a CORPUS?
“In its broadest sense, a corpus is a collection of texts
or utterances that is used as a basis
for conducting some type of linguistic investigation”
Translators usually:
■
Compile and analyse corpora for terminological researches
■
Consult corpora of parallel texts to produce a TT with the appropriate style, format,
terminology and phraseology
Electronic corpora
A corpus in electronic form is an electronic corpus, and its advantage is that it can be
manipulated by a computer (and quickly scanned and analysed!)
It must be noted that a corpus is not a random collection of texts. The texts are selected
according to explicit criteria in order to be used as a representative sample of a
particular language or subset of that language.
Representativeness is a define feature for a corpus!
Types of corpora
Given that corpora are specially designed to meet the needs of the project at hand, there
are as many different corpora as there are projects. Nevertheless, it is possible to identify
some general characteristics that corpora may have:
■
General/reference vs. specialized corpora
■
Written vs. spoken corpora
■
Synchronic vs. diachronic corpora
■
Monolingual, bilingual or multilingual corpora
■
Comparable vs. parallel corpora
■
Native vs. learner corpora
Monolingual and bilingual parallel corpus
A parallel corpus is a corpus that contains a collection of source texts in a language A
aligned with their translations into language B.
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
A monolingual corpus is one that contains text in a single language.
A bilingual parallel corpus (also called “bitext”) can be a very powerful tool for a translator
source texts aligned with their translations.
Corpus analysis tools
A corpus-analysis tool is a software used to access and display information contained in a
corpus and typically contain features that allow the user to generate and manipulate:
■
Word-frequency lists
■
Concordances
■
Collocations
Word frequency lists
The most basic feature for a corpus-analysis tool
A word-frequency list allows the user to discover
how many different words are in a corpus and how often
they appear; and it can be manipulated in many ways:
■
Lemmatized lists (words like “translate” “translated” “translating” are treated as
separate forms even though they are related. Related words are grouped under a
lemma, the total word count for the lemma is sown in the right-hand column, and the
frequency counts for individual words belonging to the lemma are shown in
parentheses.)
■
Stop lists (contains any items that a user wants the computer to ignore).
Let’s take this example (from Bowker, 2002): “I really like translation because I think
that translation
is really, really fun”. 13 words the corpus contains 13 tokens but some words appear
more than once (I, translation, really), and therefore this corpus contains only
9 different words 9 types
In a word-frequency list, the number of tokens is shown beside the type.
Concordancers
Translators not only have to be able to understand the ST, they also have to produce a TT.
Dictionaries are helpful, but in order to be able to determine how terms can be used, it is
useful to see them in context, and, preferably, in more than one context. A second feature
that is common to most corpus-analysis tools is a concordancer. A tool that retrieves all the
occurrences of a particular search pattern in its immediate contexts and displays them in an
easy-to-read format. The most common display format is known as KWIC (key word in
context) display. All of the search pattern are lined up in the centre of the screen. Typically
permit more sophisticated search patterns, allowing functions such as case-sensitive
searches (to distinguish between Polish and polish) or more characters in a search strings
(print* to retrieve print, printed, printer ecc), and searches using Boolean operators (AND,
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
OR, NOT). Is also a context search, in which another term mut appear within a userspecified distance of the search patterns (context in which printer appears within five words
of cartridge).
Bilingual concordancers
Is a tool that can be used to investigate the contents of a parallel corpus. A parallel corpous
is a corpus that contains a collection of source texts in a language A aligned with their
translations into language B. Alignment is the process whereby sections of the source text
are linked up with their corresponding translations. Alignment can take place at many
different levels: text, paragraph, sentence or even words. Bilingual concordancers employ
statistical measures in order to try to identify possible equivalents for specific search terms.
For the best results the ST and the TT must have a similar structure. Problems include cases
in which one sentence in the source text has been translated by two sentences in the target
text, or vice versa. Like monolingual concordancers, the bilingual one retrieve all
occurences of a particular search pattern in its immediate contexts search pattern can be
entered in either language. For example, if the search term “disk” is entered using English as
the search language, the tool analyzes all the possible equivalents, that includes, for
example, the French terms dique, disquette, lecture cc. We also have bilingual quert in
which the user can specify a search term in both languages.
Collocations
Many corpus-analysis tools have the ability to compute collocations, that is characteristic
co-occurrence patterns of words: words that typically “go together”. Because language is
not random, certain words tend to cluster together, and some of these clusters form
collocations. The formula commonly used for determining the likelihood that two words are
collocates is the mutual information formula (MI). The higher the MI, the stronger two
words are connected.
PROS:
•
Frequency data can be easily generated
•
Translators can see terms in a variety of contexts simultaneously
•
A great number of documents can be quickly consulted
CONS:
•
Availability and copyright can be an issue
•
Aligning text in the case of bilingual corpora is time-consuming
•
The user have to develop sensible research strategies
•
Not all tools come equipped with characters sets for all languages
4.Terminology-Management System
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
A major part in any translation project is identifying equivalents for specialized terms.
Subject fields such as computing, manufacturing, law and medicine all have significant
amounts of field-specific terminology. Researching the specific terms needed to complete a
translation is a time-consuming task. A terminology-management system (TMS) can help
with various aspects of the translator’s terminology-related tasks, including the storage,
retrieval, and updating of term records.
Effective terminology management can:
•
Help to cut costs
•
Ensure greater consistency (internally and externally)
•
Improve linguistic quality
•
Reduce turnaround times for translation
STORAGE
TMS acts as a repository for consolidating and storing terminological information for use in
future translation projects.
IN THE PAST:
•
TMSs stored information using one-to-one correspondences.
•
Users could only choose from a predefined set of fields.
TODAY:
•
TMSs use a relational model, which permits mapping in multiple language
directions.
•
TMSs adopt a free entry structure, which allows users to define their own set of
fields.
RETRIEVAL
Once the terminology has been stored,
translators need to be able to retrieve this information.
A range of retrieval mechanisms is available:
•
A simple look-up to retrieve an exact match.
•
Wildcards for truncated search (EX: comput*)
•
Fuzzy matching techniques.
A fuzzy match retrieves terms that are similar to the requested search patterns, but that do
not match it exactly.
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
ACTIVE TERMINOLOGY RECOGNITION
AND PRE-TRANSLATION
A feature of some TMSs, particularly those that operate as part of an integrated package
with word processors and translation-memory systems. It is essentially a type of automatic
dictionary look-up. Some systems also permit a more automated extension of this feature
in which a translator can ask the system to do a sort of pre-translation
or batch processing of the text.
TERM EXTRACTION
Also called “term recognition” or “term identification”. This process can help a translator
build a term base more quickly. Even if the extraction is performed by a computer,
the resulting list of candidates must be verified by a human. Semi-automatic (computeraided) process. Unlike the word-frequency lists described earlier, term extraction tools
attempt to identify multi-word units linguistic approach and statistical approach
Linguistic approach to term extraction: the tools tries to identify word combinations that
match particular part-of-speech patterns.
EX: NOUN+NOUN or ADJECTIVE+NOUN (typical in English)
In order to implement such an approach, each word in the text must by tagged with its
appropriate part of speech. Then, the tool identifies all the occurrences that match the
search. Unfortunately, not all texts can be processed this neatly:
1. Not all the combinations that match the specific patterns
can be qualified as terms.
2. Some legitimate terms may be formed according to patterns
that have not been pre-programmed into the tool.
Adopting a linguistic approach to term extraction, the tool looked for the combinations
NOUN+NOUN and ADJ+NOUN
Not all the combinations that match the specific patterns can be qualified as terms.
EX: NOUN+NOUN or ADJ+NOUN
“antivirus software”
“current status” NOISE
2. Some legitimate terms may be formed according to patterns
that have not been pre-programmed into the tool.
EX: NOUN+NOUN or ADJ+NOUN
“after-the-fact detection” SILENCE
(is legitimate but its pattern is PRE+ART+NOUN+NOUN
Statistical approach to term extraction:
the most straightforward statistical approach to term extraction is for a tool to look for
repeated series of lexical items, specifying a threshold (the number of times a series must
be repeated). If the threshold is two, a series of lexical items must appear at least twice to be
recognized.
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
Unfortunately, not all repeated series qualify as terms ( NOISE)
and not all legitimate candidates are repeated ( SILENCE)
Both linguistic and statistical approaches have drawbacks, but there is a clear advantage in
adopting the latter, statistical approach is NOT language-dependent while the linguistic
approach is.
5.
Translation-Memory Systems
One of the most important sources of information to which a translator can have access is a
large body of previous translations.
(Kay and Roesheisen, 1993, in Bowker, 2002)
Given the staggering volume of translations
produced year after year, it is quite obvious that existing translations contain more solutions
to more translation problems than any other available resources.
(Isabelle, 1993 in Bowker, 2002)
The concept of Translation Memory has existed for some time. The idea originated in the
1970s.
What is a Translation Memory? A TM is a type of linguistic database that is used to store
source texts and their translation, explicitly aligned.
A TM is a type of linguistic database that is used to store source texts and their translation,
explicitly aligned. The texts are broken down into short segments that often correspond to
sentences (often, but not always!) Translation Unit made up of a source text segment and
its translated equivalent. Most simply, a TM can be viewed as a list of source-text segments
explicitly aligned with their target text counterparts.
Translation Unit a source text segment aligned with its translated equivalent.
A TM can be viewed as a list of source-text segments explicitly aligned with their target
text counterparts. Does it ring a bell? The resulting structure of a TM is sometimes referred
to as a parallel corpus, or bitext.
Using a TM system the translator will be able to “recycle” previously translated segments.
These systems work automatically comparing new source segments against a database of
translations. If a matching segment is found,the system will propose the “old” translation
to the user, who will then decide whether to use or discard it.
SEGMENT the basic unit in a TM system. But deciding what constitutes a segment isn’t
easy!
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
SEGMENT the basic unit in a TM system. In most instances, the basic unit of
segmentation in a TM is the sentence, and this is why TM are sometimes called sentence
memories. However, not all texts are written in sentence form
(e.g. headings, table cells, etc.) Many TM systems allow the user to define other units of
segmentation in addition to sentences, which can include sentence fragments or even entire
paragraphs.
Deciding what constitutes a segment is not a trivial task! It seems easy to decide that full
sentences will qualify as segments, but how can a TM system identify sentences?
Punctuation such as periods, exclamation points and question marks are typically used to
indicate the end of a sentence but what happens in case of an abbreviation such as Mr. or
Dr.? Or in case of an ellipsis, which can appear in the middle of a sentences? Some of these
problems can be solved incorporating stop lists into the TM systems.
Another issue related to segmentation is the fact that the segmentation units used in the ST
may not correspond exactly to those used in the TT. This lack of one-to-one correspondence
can create difficulties for automatic alignment programs.
Most TM systems present the user with a number of different types of segment matches.
What is a match? Matches are correspondences between a new SL segment and one or more
“old” translations contained in the database.
The most common types are:
■
EXACT matches
■
FUZZY matches
■
TERM matches
EXACT (also called “perfect” matches) 100% identical, including spelling, punctuation,
numbers, even formatting, etc.
FUZZY when a fuzzy match is found, it means that in the database there is a segment that
is similar to the new one (the similarity can range from 1-99%, but the user can set the
sensitivity threshold – the standard is between 50 and 99%).
TERM if working in association with a term base (a terminological database), the TM
system will compare the single terms contained in the new segment with the ones in the
term base.
A Translation Memory is essentially a type of database. It is basically a software that
allows a user to store and retrieve information. However, as with any database, the
information must be provided by the user. Therefore, when the user first purchase a TM
system,
the database is empty. The system becomes useful when the translator begins to store some
data (source and target texts) in the TM.
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
How can we create a Translation Memory?
Two main ways:
1. Interactive translation: while we translate the text within the TM system, the new
TL segments are fed to and stored in the TM.
2. Post-translation alignment: if we have some source texts and their correspondent
translations (translated “in the old fashion”), we can upload them in the TM system,
ALIGN them and feed the translations to the TM.
TM can be exported and sent!
Given that a TM system allows the user to re-use previously translated work, which kind of
texts are more suitable for inclusion in a TM? The most suitable texts for a TM are
repetitive and highly specialized texts, and texts that will be updated or revised:
■
Text with internal repetitions
(the higher the percentage of repetitions, the more desirable it is to use a TMS)
■
Revisions
(amended version of a previous text)
■
Recycled texts
(sometimes referred to as external repetitions)
■
Updates
(e.g. when the client makes changes to a text that you are already translating)
According to Bowker (2002), the first thing to take into consideration is that an empty TM
is of NO use. The performance of the TM system is dependent on the scope and quality of
the existing DB and the quality of the translations store in the DB is dependent on the
translator’s skills!
PROS: it saves you time
CONS: if you can’t use the software properly, it’s time consuming!
You will need a few week’s training to be able to use a TM system in a way that it will save
you time, instead of making you lose time!
PROS: it improves consistency (internal and external)
CONS: The rigidity in maintaining the same ST’s order in the TT may affect the
naturalness of the translation
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
Different software applications store information in different formats, and Translation
Memories are no exception. The format used by any given TM is not necessarily compatible
with those of other TMs or TM systems. A standard data-exchange format for TMs was
developed through the years to solve this problem TMX. The purpose of TMX is to make it
easier to import and export data between different TM systems without losing or distorting
information.
Further considerations
■
TM systems are often quite expensive (even though the prices have been dropping
and a few free systems are emerging)
■
And they tend to need “high” minimum requirements to work properly on a PC (a
lot of RAM and a good CPU)
■
Different systems work with different formats (even though some standard are
emerging – ex: .TMX for TM)
■
Some languages are easier to process than others (especially when it comes to handle
the segmentation)
■
Using TM systems affects payments , as the clients may want to pay less for exact
and fuzzy matches (but isn’t it fair, in a way?)
■
A “full” TM is an asset, and issues of ownership may arise
6.
Other new technologies and emerging trends
6.1. New attitudes toward translation and translators
As the volume of translation increases, translators are depending more and more on
technology to help them with their task. In order to maximize the usefulness of
technology, it has become necessary to change the way in which the translation process
is viewed. In the past was often seen as an “add-on” process, and it was divorced from
the principal document-production process.
An increasing number of clients are beginnning to implement more stringent writing and
style guidelines. By asking writers to use a sort of controlled language, clients can
maximize the recyclability of texts during the translation process –> eliminating anapora,
using preferred terms, avoiding ellipses, usin punctuation consistently, all this skills can
increase the number of matches found by a TM.
Tools make easier for translators to begin working on documents even before they have
been finalized. If an author updates a document, the translator can run the updated source
text through the TM system and i twill quickly identify new or changed segments.
6.2. New types of translation work generated by technology
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
As the number of electronic products and resources increases, there is a growing demand for
translators who are able to translate media such as software applications, multimedia
products, web pages, and even on-line chat sessions.
Software localization which is the adaptation of software package to a target language and
culture, is one the fastest-growing translation markets. It involves some additional
consideration:
1. One issue that translators must deal with is the physical constrains of the screen
space. In some development environments, the width of a menu or a button can be
adjusted (to accomodate the longer french term “sauvegarder” as “save”), but in other
cases, the width is fixed and the translator must choose a term or a transparent
abbreviation that fits within the allocated space.
2. Icons or other visual elements can also be problematic. For example, whereas an
icon showing an open door may transparently capture the notion expressed by the
corresponding command “exit, the transparency would be lost if the translator chose
to translate that command into the target language using an equivalent that meant
“quit” or “terminate”.
3. Variables are another element that may cause difficulties for translators. A variable i
a character or string of characters that acts as a placeholder and is replaced by
anoyher, more meaningful string of characters when the relevant software is running.
Es pag 132 non c’ho capito troppo.
4. Standardization. If a particular product belongs to a larger family of products, it
may be necessary to standardize the terminology (use exit instead of quit).
6.3. New technology generated by new types of translation work
Other types of tools are being developed:
•
Those that check to see if a translation has been truncated because of space
restrictions.
•
Those created to separate out the material that needs to be translated from the
software code itself a translator must be able to identify and translate all relevant
material without deleting or distorting the computer code. To this end, different types
of software have been developed to assist a translators. Some of the simpler software
displays the translatable and the non-translatable material in different colours so that
translators will know where to focus their attention. More sophisticated software will
protect the tags to ensure that they cannot be deleted or edited.
6.4. Conditions required to ensure the continued succes of CAT tools
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
Haynes suggest that more developers of CAT tools should make their product freely
available to translator training institutes, noting that these developers may well benefit
financialy when the students graduate and are in a position to influence corporate
purchasing decisions regarding translation technology.
6.5. Future developments
With regard to TMs in particular, there has been concern among translators that the notion
of a “text” has been lost because the tools operae primarily at sentence level.
Macklovich and Russel also point out that text has global properties that are not easely
associated with its lower level components, including administrative information concerning
who originally translated a text the date of the translation, the client and so on.
In terms of more general developments, it is likely that there will be continued movement
away from stand-alone systems and toward a client-server architectur which facilitates
networking, thus making it possible for multiple users to share the same corpora, TMs, or
term bases.
Other improvments that are underway include extending CAT tools to support a wider
variety of languages (thai, urdu) by using encoding methods such as unicode and designing
new standards and filters to support a wider variety of file formats, including formats using
tags (html, xml) without losing the formatting of original source text.
Transrouter will evaluate the uselfulness of available resources in the context of specific
translation project based on information supplied by the user (a translator or translation
manager) and on the automatic analysis of text characteristics using its component features.
These component features will include a cost estimator, a word counter, a sentence-length
estimator and a sentence-semplicity checker.
Importanti capitoli 4-5 da leggere bene sul libro.
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)
Document shared on www.docsity.com
Downloaded by: naomi.pelliccioni1 (naomi.pelliccioni0500@gmail.com)