The program breaks the source text (the text to be translated) into segments, looks for matches between segments and the source half of previously translated source-target pairs stored in a translation memory, and presents such matching pairs as translation candidates. The translator can accept a candidate, replace it with a fresh translation, or modify it to match the source. In the last two cases, the new or modified translation goes into the database.
Some translation memories systems search for 100% matches only, that is to say that they can only retrieve segments of text that match entries in the database exactly, while others employ fuzzy matching algorithms to retrieve similar segments, which are presented to the translator with differences flagged. It is important to note that typical translation memory systems only search for text in the source segment.
The flexibility and robustness of the matching algorithm largely determine the performance of the translation memory, although for some applications the recall rate of exact matches can be high enough to justify the 100%-match approach.
Segments where no match is found will have to be translated by the translator manually. These newly translated segments are stored in the database where they can be used for future translations as well as repetitions of that segment in the current text.
Translation memories work best on texts which are highly repetitive, such as technical manuals. They are also helpful for translating incremental changes in a previously translated document, corresponding, for example, to minor changes in a new version of a user manual. Traditionally, translation memories have not been considered appropriate for literary or creative texts, for the simple reason that there is so little repetition in the language used. However, others find them of value even for non-repetitive texts, because the database resources created have value for concordance searches to determine appropriate usage of terms, for quality assurance (no empty segments), and the simplification of the review process (source and target segment are always displayed together while translators have to work with two documents in a traditional review environment).
If a translation memory system is used consistently on appropriate texts over a period of time, it can save translators considerable work.
Translation memory managers are most suitable for translating technical documentation and documents containing specialized vocabularies. Their benefits include:
The main problems hindering wider use of translation memory managers include:
The use of TM systems might have an effect on the quality of the texts translated. Its main effect is clearly related to the so-called “error propagation”: if the translation for a particular segment is incorrect, it is in fact more likely 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.
Traditionally, two main effects on the quality of translated texts have been described: the “sentence-salad” effect (Bédard 2000; cited in O’Hagan 2009: 50) and the “peep-hole” effect (Heyn 1998). The first refers to a lack of coherence at the text level when a text is translated using sentences from a TM which have been translated by different translators with different styles. According to the latter, translators may adapt their style to the use of TMs in order for these not to contain intratextual references, so that the segments can be better reused in future texts, thus affecting cohesion and readability (O’Hagan 2009).
There is a potential, and, if present, probably an unconscious effect on the translated text. Different languages use different sequences for the logical elements within a sentence and a translator presented with a multiple clause sentence that is half translated is less likely to completely rebuild a sentence. Consistent empirical evidences (Martín-Mor 2011) show that translators will most likely modify the structure of a multiple clause sentence when working with a text processor rather than with a TM system.
There is also a potential for the translator to deal with the text mechanically sentence-by-sentence, instead of focusing on how each sentence relates to those around it and to the text as a whole. Researchers (Dragsted 2004) have identified this effect, which relates to the Automatic Segmentation feature of these programs, but it does not necessarily have a negative effect on the quality of translations.
Note that these effects are closely related to training rather than inherent to the tool. According to Martín-Mor (2011), the use of TM systems does have an effect on the quality of the translated texts, especially on novices, but experienced translators are able to avoid it. Pym (2013) reminds that “translators using TM/MT tend to revise each segment as they go along, allowing little time for a final revision of the whole text at the end”, which might in fact be the ultimate cause of some of the effects described here.