Errors in translation can go beyond damaging your reputation—they may lead to life-altering consequences. In regulated industries, for example, errors in translating medical device instructions or pharmaceutical labels could jeopardize patient health, while inaccuracies in legal contracts might result in unintended breaches of law.
In these high-stakes fields, ensuring accurate and reliable translations is essential to maintain compliance, protect reputations, and prevent costly mistakes.
Proper due diligence is crucial: even skilled translators can struggle without a clear process, resulting in errors and unmet client expectations. Similarly, a strong process without subject matter expertise can lead to challenges, especially in specialized fields.
To address these challenges, a robust approach to risk mitigation is essential.
Risk Mitigation
Risk mitigation in translation ensures accurate and reliable communication across languages and formats. One effective way to de-risk translations is by using consistent quality assurance to identify and address potential errors before they reach the end-user or target audience.
This practice helps maintain the message’s integrity, reduces the possibility of misinterpretation, and safeguards the organization’s reputation. Organizations can minimize risks and ensure high-quality translations by:
- Implementing standardized review procedures to catch errors before they reach the audience.
- Leveraging technology for consistency, accuracy, and efficiency.
- Engaging skilled linguists with subject matter expertise to maintain cultural relevance and accuracy.
By combining these strategies with clear metrics and processes to measure translation accuracy, consistency, and relevance, we can proactively manage risks and deliver translations that meet the highest standards of precision, reliability, and quality.
How we measure translation quality
The first approach for successful implementation is understanding our client’s needs; this includes terminology, scope, tone, style, and context. Once we have this information, we can create the corresponding references, instructions, and processes to set us up for success.
We measure quality on a customer-by-customer basis using the following frameworks as a basis:
1. Client-Specific Metrics
Client-specific metrics are based on the Multidimensional Quality Metrics (MQM) model to identify areas for linguistic quality improvement.
2. Quality KPI
This KPI is evaluated by analyzing word counts and the number of errors, and we have set a stringent internal quality target of 99.15%. While this target is what drives our internal standards, we also work with customer-specific quality KPIs based on their requirements, incorporating different data points to tailor our approach to their needs.
3. On-Time Delivery
This metric is measured for timely completion, which is critical to our customers’ success. Our internal target for on-time delivery is 97%, a benchmark that reflects our commitment to efficiency and reliability. However, we are flexible and work closely with customers to adapt delivery metrics based on the specific demands of their projects.
4. Additional Metrics
Metrics may include quality assessments by the number of files, projects, or other criteria as requested.
By combining these measurement frameworks with a well-structured workflow, we can identify and address potential risks early in the process. However, achieving this level of precision also requires leveraging advanced language technologies.
The role of language technologies in measuring quality
Automated tools have been ensuring error detection long before Generative AI became popular. While avoiding errors is ideal, the true priority is catching and fixing them before they ever reach the client.
Many automated QA checks, some included in CAT tools, can be created to address known issues or customer preferences. For example, if a customer has specific number formatting requirements, automatic QA checks can detect mismatches or errors before delivery.
At TOPPAN Digital Language, our proprietary technology platform, STREAM, improves the multilingual content workflow with features including quality analysis and assurance tools to mitigate risk and improve consistency in terminology and tone of voice.
Impact of AI on quality estimation and assurance
AI has greatly impacted translation quality, with advancements accelerating dramatically in just the past few months. One of the key areas where AI adds value is in quality estimation (QE), which involves assessing the accuracy and reliability of translations.
While this is great for certain industries or target uses, in regulated industries, it’s crucial that subject matter experts validate the results of the quality estimation and that the content is adequately translated.
One of the pitfalls of AI to look out for is hallucinations. These occur when the generated content is inaccurate or completely fabricated – examples that have been made infamous with the rise of large language models like Chat GPT.
For this reason, AI output and estimations should always be critically reviewed, not taken at face value. A human-in-the-loop approach ensures a seamless collaboration between machine and language specialists, improving translation efficiency, quality, and time to market.
Where AI truly makes an impact is automating repetitive tasks and reducing potential quality issues, rather than replacing linguists altogether. This allows linguists and language specialists to focus on adding value to each step of the translation process.
In conclusion
In regulated industries, where accuracy is paramount, translation quality is about mitigating risks and safeguarding trust, not just avoiding errors. By creating robust customer-centric measurements for quality, prioritizing rigorous quality assurance, leveraging advanced technologies, and collaborating with subject matter experts, we can ensure that translations uphold the highest standards of accuracy and integrity.
While AI offers powerful tools for streamlining processes, true quality lies in the balance between technology and human expertise.
The future of translation quality isn’t just in the tools we use but in the thoughtful application of those tools to meet the unique needs of every customer and context.