Sweep away messy translations: How AI is automating post-editing
5 mins
Artificial intelligence (AI) has transformed how we interact with technology - even in everyday household appliances. Take robot vacuums, for example: once basic cleaners, they now map rooms, navigate autonomously, and clean efficiently on a schedule. Some models even learn over time, recognizing how frequently dirt is detected in each space and adjusting their cleaning accordingly.Ìý If crumbs frequently accumulate near the kitchen table or mud builds up by the front door, an advanced AI-powered vacuum can detect these patterns and automatically increase suction or perform extra passes - improving its performance over time and reducing the need for human intervention.
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Now, imagine applying this self-improving AI to machine translation. Neural machine translation (NMT) has been one of the most impactful AI-driven technologies in the translation industry. Yet, much like early robot vacuums, it still relies on human effort to refine results through machine translation post-editing (MTPE).
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But what if AI could go further - evaluating translations, identifying weak areas, and refining them automatically?
Introducing self-improving AI for higher quality translations
At Language Weaver, we’ve tackled this challenge with automatic post-editing, an AI-driven capability that intelligently identifies and improves translations needing refinement in real-time. This dramatically enhances machine translation quality, increases scalability, and reduces reliance on human intervention.
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By leveraging recent advances in Large Language Models (LLMs), along with broader research on AI, we’ve developed a powerful, fully automated post-editing capability - available with our Generative Language Pairs - that enhances translations much like AI-powered vacuums improve their cleaning performance.
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At the core of this innovation are three specialized AI models, each optimized for a distinct task: translation, text analysis, and text generation. By seamlessly combining these capabilities, we’ve created a process that translates input text, detects areas that need refinement, and automatically enhances them.
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With this cutting-edge technology, companies can translate high volumes of content at the speed of traditional machine translation (MT) while leveraging Generative AI to enhance quality and fluency - pushing translation beyond its current limits. By minimizing human post-editing, businesses gain greater efficiency, lower costs, and faster time to market - without compromising on quality.
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Let’s take a closer look at the three AI models we combine to deliver automatic post-editing capabilities:ÌýÌý
- Translate with auto-adaptive neural machine translation. Optimized to deliver fast, accurate and relevant translations at scale, it continuously learns from external inputs, such as translation memory data, bilingual dictionaries, and real-time post-editing feedback - refining its output over time through a built-in feedback loop.
- Evaluate with machine translation quality estimation (MTQE). Calibrated using human-labeled examples and annotated dataÌý Ìýfrom our in-house expert linguistic teams, MTQE automatically assesses each translated sentence, categorizing it as good, adequate, or poor.Ìý ÌýBy detecting and flagging, lower-quality translations, it helps direct improvement efforts where they are needed most.Ìý
- Refine with our privately hosted Large Language Model (LLM). Securely hosted within the same infrastructure as our MT and MTQE, the LLM steps in to correct errors and enhance fluency - delivering automatic post-editing without human intervention.
How do these technologies work together?
The process begins with the source text being translated using auto-adaptive MT. The translation is then evaluated by an MTQE engine, which rates it as good, adequate, or poor. If the rating is deemed adequate or poor, the translation undergoes further refinement - or post-editing - by a private LLM.Ìý The LLM’s updated translation is then re-evaluated by the MTQE engine for validation.
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The image shows a file opened in the Language Weaver editor, where you can hover over an individual segment to view detailed results of the iterative evaluate/edit task loop. Here, a segment has undergone three iterations, progressing from 'poor' to 'good'.
One key advantage of building a capability from three separate modules - translation, evaluation, and refinementÌý - is that we can fine-tune not only the individual components but also how they work together. The process of iterating the evaluate/refine task loop is repeated up to three times, incorporating additional context from the source document as needed to increase the chance of enhanced translation accuracy. Edits made by the LLM are accepted only if they improve the original translation’s MTQE score. After the final iteration, the translation is ready for review.
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Human linguists can step in when necessary, and any feedback is automatically integrated into the auto-adaptive MTÌý to improve future translations – creating a continuous feedback loop. Rather than relying solely on human intervention, this approach enhances productivity gains by trusting the scores. For example, you may choose to skip reviewing segments already rated as 'good' and instead spend time on refining translations that remain as 'poor' or 'adequate' after the automatic post-editing process. The end result of automatic post-editing is higher-quality translations with significantly less manual intervention.
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The image shows an overall report on the outcome of the improved segment from the auto-post editing process.
Why automatic post-editing is a game-changer
As you can imagine, combining three AI-driven technologies to address the challenge of MTPE offers numerous advantages:
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- A self-improving feedback loop. Since Language Weaver tracks all quality assessment outcomes and automated edits, the by-product of the translate/evaluate/refine sequence serves as a valuable source of feedback for the MT engine.
The auto-adaptive MT monitors incoming edits and automatically updates their models to reflect observed improvements. This positive automatic feedback loop ensures that output from the auto-adaptive MT continues to improve in quality over time. This process is shown in the diagram below.Ìý
- Human-in-the-loop workflows. While automatic post-editing doesn’t require human intervention, it offers the option for users to review the translation output through Language Weaver's simplified editing interface.
It can be applied across all use cases where traditional MT is used, as it does not alter how translations are consumed by external systems and workflows. In localization, where some level of human intervention may still be needed (especially for regulatory content), it seamlessly integrates into existing workflows to reduce the post-editing burden on human linguists. For instance, Language Weaver integrates with our CAT tool, Trados Studio, enabling translators to leverage automatic post-editing within a professional translation environment.
Post-edits made by human linguists are also incorporated into the feedback loop. This adaptive learning process ensures that translations become increasingly accurate and refined over time, meeting increasing translation requirements.ÌýÌý - Enterprise-grade security and privacy are at the core of automatic post-editing, which operates entirely within Language Weaver’s private, secure environment. Data protection is embedded across all layers of its architecture, development lifecycle, and policies.
Our automatic post-editing capability leverages a dedicated LLM hosted by ¾ÅÉ«ÊÓƵ, allowing us to fine-tune performance, ensure top-tier data security, and maintain a predictable cost structure. It is safeguarded against third-party API instabilities, ensuring that no data is ever sent to public LLMs for model training. -
The image shows MTQE scores for each segment in Trados- Integration with business applications is available through all of Language Weaver’s existing integration options, enabling seamless connectivity with platforms such as CMS, TMS and KBS. This ensures more accurate automatic translations wherever needed.ÌýÌý
- AI workflow. This capability goes beyond prompt-based AI, it is an AI workflow that calls on an LLM for multiple steps, using structured sequences of operations to automate and streamline complex business processes while improving efficiency. What’s especially exciting is thatÌý this serves as a precursor to agentic AI, which can deviate from predefined workflow steps, proactively reason, and make decisions across domains - opening new possibilities for the future.Ìý
How can automatic post-editing benefit your organization?
Just as a robot vacuum offers tangible benefits by improving its ability to clean up dirt, auto post-editing provides similar advantages by refining and enhancing messy translations. It benefits not only those using machine translation in the translation process but also those applying machine translation in other use cases.
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For enterprises and language service providers with translation processes, automatic post-editing improves ROI, increases efficiency, and reduces costs by driving significant productivity gains. This is achieved by optimizing MTPE workflows with self-improving translation quality and a continuous feedback loop. Automatic post-editing reduces manual effort and allows limited resources to be focused where they matter most - refining segments scored adequate or poor.Ìý Additionally, human-in-the-loop support ensures the right level of quality is delivered, particularly for content requiring regulatory compliance.
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Optimizing post-editing is a major opportunity not only for those involved in the translation process but also for organizations without localization departments. ByÌý combining auto-adaptive machine translation and LLMs, automated translation becomes more effective even in scenarios with minimal human intervention. This is particularly valuable for high-volume use cases like legal eDiscovery and digital forensics - especially where time-to-market or time-to-insight is the priority.
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Additionally, the self-improving feedback loop ensures that even without traditional translation resources like translation memory, termbases, or human linguists, you still benefit from continuous quality improvements. The LLMs enhance the MT output over time by incorporating feedback and refinements automatically. With LLM-driven refinements, organizations that previously lacked the data to train adaptable MT models can now jump-start their translation process, benefiting from a virtuous improvement cycle.
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Automatic post-editing is available for Language Weaver Cloud, supporting over 20 Generative Language Pairs, with more set to be rolled out regularly.
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Ready to enhance your translations with AI-powered post-editing? Contact us to discuss how Language Weaver can meet your needs.
Camille Avila
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