What is computer-assisted translation?
Computer-assisted translation, or CAT, uses software to support a human translator in the entire translation process. Computer-assisted translation software supports the significant parts of translation, like managing translated content and editing. A computer-assisted translation tool helps translators create, edit, manage, and store translation.
During the translation process, a human translator deducts the meaning of each word, while computer-assisted translation tools help speed up the process. Computer-assisted translation saves a translator’s time by not having to retype the same translations in different texts. Many computer-assisted translation tools can translate source file formats in a single editing environment.
Spell-check and spell correction are among the most commonly used applications of CAT. They split large documents into small segments, which are stored in a database. This is called translation memory (TM).
Benefits of using computer-assisted translation
Computer-assisted translation has helped the localization industry in various ways.
- Consistent translations: When content is translated, the abbreviations, names, or terms have to be used consistently. The TM aids in maintaining consistency by suggesting previously translated strings of text that are already stored in the database. The phrases are stored in translation memory’s database over time, helping speed up the translation process by suggesting recurring words or phrases, thus eliminating the need to type them. The more TM is stored, the better the quality of the translated document.
- Reduced translation time: Computer-assisted translation helps translate content in less time than a manual process. Thus quality translated content can be obtained quickly.
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Improved accuracy: With computer-assisted translation, translators
can increase their accuracy when processing content. The tool stores text in segments and thus helps translators to search the saved segments, thus replacing the copy without manually making any changes to the translation process.
Basic elements of computer-assisted translation
There are a few elements that form computer-assisted translations.
- Translation memory (TM): This is a database of text in original and translated languages. It divides the text into segments and stores the content in a database of translated parts. When a translator proceeds with the translation, TM shows the recommendations for translated words and phrases.
- Terminology database: This database helps translators use consistent terminologies throughout the translation. The terminology database allows translators to add terms and verify if they are consistently translated throughout the content.
- Bitext aligner: This tool helps align translated and original text side by side, allowing the translator to compare both versions efficiently.
- Full-text search: These tools, also called indexers, help translators search different kinds of translated documents.
Computer-assisted translation vs. machine translation
Computer-assisted and machine translation majorly differ in the amount the translators are involved in. Translators do not get directly involved in the machine translation process. However, they play an essential role in post-editing for computer-assisted translations.
Computer-assisted translation tools depend on TMy to provide translators with suggestions. In short, computer-assisted translation helps translators to work faster, more efficiently, and precisely. In contrast, machine translation automatically translates the content without any translator involved.
Spell and grammar checkers are typical examples of computer-assisted translation. Computer-assisted translation tools are helpful for niche translators focused on improving their translation skills, especially in industries involving agriculture, legal, and medicine. Some common machine translation tools include Google Translate, Amazon Translate, and DeepL. These tools are mainly used when translating large volumes of text.
Shalaka Joshi
Shalaka is a Senior Research Analyst at G2, with a focus on data and design. Prior to joining G2, she has worked as a merchandiser in the apparel industry and also had a stint as a content writer. She loves reading and writing in her leisure.
