• Ensuring that the translated documents are consistent, including common definitions, phrasings and terminology. This is important when different translators are working on a single project.
• Accelerating the overall translation process; since translation memories "remember" previously translated material, translators have to translate it only once.
• Reducing costs of long-term translation projects; for example manuals, warning message or series of documents need to be translated only once and can be used several times.

Main Obstacles

• The concept of "translation memories" is based on the premise that sentences used in previous translations can be "recycled". However, a guiding principle of translation is that the translator must translate the message of the text, and not its component sentences.
• Translation memory managers do not easily fit into existing translation or localization processes. In order to take advantages of TM technology, the translation processes must be redesigned.
• Translation memory managers do not presently support all documentation formats, and filters may not exist to support all file types.
• There is a learning curve associated with using translation memory managers, and the programs must be customized for greatest effectiveness.
• In case where all or part of the translation process is outsourced or handled by freelance translators working off-site, the off-site workers require special tools to be able to work with the texts generated by the translation memory manager.
• Full versions of many translation memory managers can cost from $1,000 to $2,500 (US) per seat, which can represent a considerable investment. However, many developers produce free or low-cost versions of their tools with reduced feature sets that indvidual translators can use to work on projects set up with full versions of those tools. (Note that there are freeware and shareware TM packages available, but none of these has yet gained a large market share.)
• The costs involved in importing the user's past translations into the translation memory database, training, as well as any add-on products may also represent a considerable investment.
• Maintenance of translation memory databases still tends to be a manual process in most cases, and failure to maintain them can result in significantly decreased usability and quality of TM matches.
• As stated previously, translation memory managers may not be suitable for text that lacks internal repetition or which does not contain unchanged portions between revisions. Technical text is generally best suited for translation memory, while marketing or creative texts will be less suitable.
• The quality of the text recorded in the translation memory is not guaranteed; if the translation for particular segment is incorrect, there is the possibility that the incorrect translation will be reused the next time the same source text, or a similar source text, is translated, thereby perpetuating the error.

Functions of a Translation Memory (TM)

IMPORT

It is very important to recognize punctuation in order to distinguish for example the end of sentence from abbreviation. So markup is a kind of pre-editing. Usually, the materials which have been processed through translators' aid programs contain mark-up, as the translation stage is embedded in a multilingual document production line. Other special text elements may be set off by mark-up. There are special elements which do not need to be translated, such as proper names and codes, while others may need to be converted to native format.

The base form reduction is used to prepare lists of words and a text for automatic retrieval of terms from a term bank. On the other hand, syntactic parsing may be used to extract multi-word terms or phraseology from a source text. So parsing is used to normalise word order variation of phraseology, this is which words can form a phrase.

Its purpose is to choose the most useful translation units. Segmentation is like a type of parsing. It is done monolingually using superficial parsing and alignment is based on segmentation. If the translators correct the segmentations manually, later versions of the document will not find matches against the TM based on the corrected segmentation because the program will repeat its own errors. Translators usually proceed sentence by sentence, although the translation of one sentence may depend on the translation of the surrounding ones.

It is the task of defining translation correspondences between source and target texts. There should be feedback from alignment to segmentation and a good alignment algorithm should be able to correct initial segmentation.

It can have as input a previous dictionary. Moreover, when extracting unknown terms, it can use parsing based on text statistics. These are used to estimate the amount of work involved in a translation job. This is very useful for planning and scheduling the work. Translation statistics usually count the words and estimate the amount of repetition in the text.

EXPORT

; Exact match

We talk about exact matches when the match between the current source segment and the stored one has been a character by character match.

When the match has not been exact, it is a fuzzy match. Some systems assign percentages to these kind of matches. Those figures are not comparable across systems unless the method of scoring is specified.

UPDATING

; Automatic retrieval

A TM features automatic retrieval and evaluation of translation correspondences in a translator's workbench.

Exact matches come up in translating new versions of a document. When you translate automatically, you cannot check the translation against the original so if there are any mistakes in the original, they will carry over.

NETWORKING

External links

References and interesting links