Machine translation post-editing: connecting technology, the human factor and ZELENKA practice

ZELENKA Company in collaboration with AI

18. 11. 2025

In recent years, the translation industry has undergone a dramatic change. Thanks to the rapid development of artificial intelligence, neural networks and machine translation tools (MT), translation has become more accessible, faster and more efficient. However, a fundamental question remains: can machine translation replace a human translator? The answer is not yet, but it can significantly assist them in their work. Post-editing – that is, the human editing of machine-translated text – plays a key role in this process.

Post-editing is a process that ensures the output of a machine translation meets the linguistic, stylistic and semantic standards required for its specific purpose. It serves as a bridge between the efficiency of machine translation and the quality of human work. This article focuses on defining the concept of post-editing, outlining its types, best practices and challenges, as well as how this discipline is evolving in the context of modern localization practice at ZELENKA Czech Republic s.r.o.

Types of post-editing

We distinguish two main types of post-editing:

Basic post-editing (light post-editing)

The goal of basic post-editing is to produce a functionally correct and understandable text that is sufficient for basic comprehension. funkčně správného a srozumitelného textu, který je postačující pro základní porozumění. Its grammar, style and terminology do not need to be perfect, but it is important that the meaning is clear and easy to understand. This type is often used for texts that are not intended for publication – for example, internal communication or informational translations.

Characteristics:

• Only major errors are corrected.
• The style and refinement of the text are not a priority.
• The translation does not need to be fully idiomatic.

Full post-editing

The goal of full post-editing is to produce a text that is comparable in quality to a human translation. The post-editor must correct not only content errors but also style, tone, terminology and consistency. Such a text can be published and used, for example, in legal contexts or technical documentation.

Characteristics:

• Accuracy, naturalness and style are emphasised.
• Terminology and factual correctness are verified.
• The text meets the standards for the target language and the specific field.

The post-editing process – step by step

1. Input analysis
   The post-editor first evaluates the quality of the machine translation output. If the translation is very poor, it may be more efficient to translate the text without using a machine translator at all. The analysis also includes assessing which MT engine was used.
2. Defining the goal and requirements
   Before starting, it is crucial to understand the purpose of the translation: whether it is for example intended for use internally, for publication, on a website or as a legal document. The type of post-editing, the style and the required terminological accuracy all depend on this.
3. Text editing
   At this stage, the post-editor makes the actual corrections – fixes grammar and syntax, translates idioms, corrects prepositions, makes terminology accurate and fits the target-language style to the text’s purpose.
4. Consistency and terminology check
   The post-editor uses terminology databases, translation memories and QA tools (e.g., QA checkers in CAT tools) to ensure terminological consistency throughout the text.
5. Final review and quality control
   The final check includes a linguistic review as well as verification of formatting, correct use of tags and compliance with the client’s instructions.

Quality and efficiency measurement

The quality of post-editing is often evaluated using metrics such as BLEU, TER nebo HTER, which compare the machine output to a reference translation. In practice, however, human evaluation in the form of LQA (Linguistic Quality Assurance) is increasingly used. It takes into account the naturalness, consistency, and adequacy of the translation in context.

From a productivity standpoint, post-editing is typically about 30-50% faster than traditional translation. An experienced post-editor working with a well-trained MT system can achieve up to double the productivity.

Post-editor skills

Post-editing requires different skills than traditional translation. Post-editors must:

• recognise errors and correct them efficiently,
• be able to quickly assess the quality of MT output,
• have strong language and stylistic skills,
• understand the context and purpose of each text.

Additionally, post-editors must be technically literate – able to navigate CAT and QA tools as well as terminology databases.

Challenges and ethical considerations

The growing use of machine translation brings new challenges:

• Uneven output quality – Not all languages and subject areas have equally well-trained models.
• Loss of style and nuance – MT often fails to convey subtle cultural and stylistic differences.
• Copyright and confidentiality issues – Texts entered into public translators may be stored and used for further model training.
• The role of translators – There is growing concern that the translator’s role may be reduced to merely “fixing machine errors.” On the other hand, a new profession is emerging – that of the post-editor, who combines linguistic and technological skills.

Transparency remains a key ethical consideration: clients should always be informed whether a translation was machine-generated and to what extent it was edited by a human.

Post-editing today is an indispensable element of translation practice. It allows for the effective use of machine translation while preserving quality and the human dimension of language communication. Even as technology advances by leaps and bounds, human intervention remains essential – especially where decisions about precise meaning, tone and cultural context are required.

Post-editing is not a step backward, but rather a step forward – toward a smarter form of translation. It teaches us that language is not merely a mechanical conversion of words, but a living tool of thought, culture and empathy, deserving human care even in the age of artificial intelligence.