Language data clean-up

Clean language data is crucial for your language processes.

With oneCleanup, oneword creates a clean data foundation for consistent translations, clear terminology and the productive use of artificial intelligence.

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How clean language data can make your day-to-day work easier

As translation memories and terminology databases grow over the years, ambiguities arise that become apparent in day-to-day work: more research, more coordination, more rework. Inconsistent segments, outdated phrasing and duplicate entries increase the amount of correction required after each translation and slow down processes that should actually run smoothly.

Clean language resources make a noticeable difference. Translation output is more consistent, existing content can be used reliably and your terminology is used consistently – in every project we carry out for you. At the same time, processed datasets lay the groundwork for artificial intelligence and machine translation systems to deliver reliable and company-specific results. After all, language models are only as good as the data on which they are based.

Which databases you can optimise specifically

The focus is on two language resources that directly influence translation quality and the amount of work involved: Translation memories and terminology databases. Both are constantly evolving in day-to-day business operations and, without targeted attention, become less reliable.

Would you like a clear assessment of how much potential for optimisation lies within your language data?

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Cleaning up translation memories

Outdated phrasing, duplicates and fragmented segments can have a negative impact on any new translation output. We clean up your translation memory databases to improve the quality of matches, reduce the amount of manual post-editing required and ensure that existing translation outputs can once again be put to productive use. The result: a translation memory that serves as a resource and can, for example, be integrated directly via oneCAT.

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Cleaning up terminology data

Ambiguous terminology and a lack of usage guidelines waste time during translation, coordination and the use of AI systems. Terminology alone accounts for up to 45% of the post-editing work involved in AI translations. We consolidate your terminology databases to ensure that entries are unambiguous, usage rules are applied, and artificial intelligence applications are provided with reliable guidelines. The terminology clean-up can then be managed in a structured manner within a database solution such as oneTerm, ensuring consistent results during day-to-day operations.

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This is how we support you through the clean-up process

Clean-up projects are not isolated, one-off measures. We look at your language resources within the context of your overall translation infrastructure and work with you right from the start to identify which steps will have the greatest long term impact. A smaller, cleaned database produces better results than large, unfiltered volumes of data.

1. Analyse your existing data

Together, we’ll gain a clear overview of your data sources, structures and usage patterns. You will receive a detailed analysis of where there is potential for cleaning up and which steps will have the greatest impact.

2. Set priorities

Based on your current projects and objectives, we will work with you to determine which areas to tackle first, without disrupting your day-to-day operations.

3. Data clean-up

Duplicates, fragments and outdated content are systematically cleaned up. As a result, your teams will be working with resources that are reliable, generate fewer queries and really take the strain off day-to-day operations.

4. Put the results to practical use

You will receive pre-processed databases that can be integrated immediately into ongoing translation processes and form a solid foundation for MT and AI applications.

With oneCleanup, we analyse your data, identify potential for cleaning up and guide you through the process step by step, without disrupting your ongoing projects.

FAQs

What is the difference between TM clean-up and terminology clean-up?

Both data sources have different structures and requirements. Translation memories are optimised for segment and match quality. Terminology databases are consolidated in such a way that terms, usage rules and entry logic are clear and unambiguous. Both can be commissioned separately or in combination.

When is the right time to carry out a clean-up?

Typical triggers for a clean-up project include a consistently high volume of post-translation editing, low match rates due to duplicates, ambiguous terminology, an impending change of tool or service provider, and the planned introduction of artificial intelligence or machine translation processes. As a general rule: The sooner existing data sets are processed, the less work is required in ongoing projects.

Does language data need a clean-up for artificial intelligence applications?

Language data is ideal for fine-tuning a large language model so that an AI system can deliver results tailored to a specific organisation. To ensure that the “garbage in, garbage out” principle does not apply, the quality of the language data is crucial. Reliable glossaries can be created by cleaning up terminology, thereby significantly improving the quality of the machine’s initial output. Cleaned TM data is used as tuning data to refine the large language model’s output. Before adapting an LLM for a specific company, the potential for cleaning up the language data should therefore always be assessed.