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Translation Tools: What is a Segment?

Computer Assisted Translation (CAT) Tools: What is a Segment?

CAT tools (Computer Assisted Translation) split a source text into manageable units — each one is known as a ‘segment’. The software then builds databases of equivalent segments in different languages. The databases of these matching segments form a Translation Memory (TM).

Find out more: What is Computer Assisted Translation?

What is a translation segment?

A segment is the basic semantic unit of a text. Although a segment could be an entire sentence, it is more usually a small group of words: ‘the red house’, for example, or ‘eighty-three’.

Once the CAT tools have divided the text into segments, the translator goes to work, translating the segments one by one. Once a segment has been translated, the TM ‘learns’ that translation, and the next time a text is put into that TM, it will search for any segments it has already learned. For example, the TM learns that ‘the red house’ means ‘la casa roja’ in Spanish and, the next time that it comes across ‘the red house’ in an English to Spanish translation, it automatically suggests the translation it already knows.


We know this suggestion as a ‘candidate’. Some TMs only search for identical candidates. Other TMs will also retrieve segments which are similar. If a segment is similar but not identical to one the TM already knows, it will flag this as a ‘fuzzy match’. A fuzzy matching algorithm calculates how similar the already-translated-segment — the fuzzy match — is to the sentence in the source text, and will indicate this appropriately, typically using a colour-based code.

Working with a TM

Having fed the source text into the TM, the translator then has various possible ways to deal with candidates, fuzzy or otherwise. In the case of an identical candidate, they will often do no more than check it before they click ‘accept’. A fuzzy candidate generally requires a closer analysis and some adjustment before it is accepted.

Let’s take an example. Imagine the TM recognised the segment ‘Dear Sir’; if you entered another document which contained the segment ‘Dear Sir/Madam’, it would suggest the translation that it had already learned for ‘Dear Sir’, indicating that this suggestion was a fuzzy match.

The translator would then decide whether to translate the new segment entirely from scratch, or adapt the TM’s suggestion. In this case, they would probably take the fuzzy match and add the extra word.

TMs are especially useful for storing industry-related terminology, guaranteeing a precise and accurate translation of technical vocabulary, regardless of which translator worked on a given project.

Find out more: Optimising with Translation Memory

New segments (which don’t appear in the TM)

Segments which have no existing match in the TM are translated ‘manually’. Once done, the freshly-translated segment is stored in the TM for use in future texts. Or maybe later on in the text at hand.

So, the translator just has to translate the first example of that segment. The TM will automatically suggest the match each time it occurs later on in the same text.

Graphic showing segment(s) in a MemoQ screen (CAT tools).


  1. Source language
  2. Target language
  3. Segmentation (sorted and filtered)
  4. Percentage match with translation database
  5. Translation Memory entries
  6. Suggestions from terminology database
  7. Finished translation exports to your preferred format
  8. TM and Glossary exported and saved for future use

Our methodology

At Quicksilver Translate we build a Translation Memory for each customer. We use this TM exclusively with that client’s documents. Therefore the TM quickly ‘learns’ the customer’s translation preferences. Project managers checks the translation of each new segment before saving them in the TM as part of our quality control process and, in order to ensure that we do not bury mistakes inside the TM which could be repeated in future documents.

Using a TM guarantees consistency throughout and between projects, speeds up the process to facilitate parallel releases in multiple languages, permits linguists to focus on semantics and style, and reduces overall costs. TMs also allow greater flexibility in translation, adapting themselves to the needs of different clients, translators, and different contexts.

Find out more: Different levels of Translation Quality and Pricing

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