In 2023, Nimdzi’s found in a survey that respondent LSPs used machine translation (MT) in 25% of their translation projects. The next year, Smartling released its 2024 State of Translation Report showing that their clients had increased the use of MT in their projects to 40% compared to 14% in 2021. These numbers indicate the rise of MT, and now, LLMs in translation workflows. With recent developments, the shift went from how to implement MT/LLMs to how to optimize them, so here are 4 strategies to balance cost and quality in MT deployment.
1. Hybrid Workflows
The use of MT raw output or post-edited content depends on the purpose of translation. Usually, when projects require good quality and high speed, combining human language professionals and MT is the best solution. Here are some of its main key points (for more on this topic, see our articles on MTPE).
- Benefits: Hybrid translation workflows combine the efficiency of machine translation with the accuracy of human post-editing. This approach is particularly effective for simple text, guides, technical content, or even more creative content in high volumes. In this case, machine translation can handle bulk work, and human translators can focus on critical or complex content.
- Cost Savings: MT can significantly decrease translation costs. Automating repetitive tasks with MT and reserving human expertise for high-value or creative content, businesses can obtain a balance between cost, speed and quality.
- Quality Enhancement: Despite how advanced MT is, human post-editing ensures that the final output is culturally relevant and accurate. It also ensures that is following style guidelines, addressing the limitations of machine translation alone.
2. Optimizing Resource Allocation
MT alone will only reduce costs to a certain point; to maximise MT capabilities, the best is putting effective project management strategies in place. Resource allocation matrixes are only one of the tools that can help identify when and where to use MT.
What is Resource Allocation?
A resource allocation matrix is a tool used in project management to visualise tasks against available resources, ensuring efficient allocation and minimising waste.
Application in Localization
Prioritise tasks based on their criticality and resource availability. This involves assigning human translators to complex tasks and using machine translation for simpler ones while taking into account costs, times, other technology at hand, etc.
Efficiency Gains
This approach helps streamline workflows, reduce bottlenecks, and ensure that resources are used effectively, leading to faster project completion and cost savings.
Source: Sciforma.com
3. Implementing Continuous Improvement
We should always aim for constant improvement of processes. There is always something to learn from and processes that might work better. Implementing some process optimization techniques into localization might help balance quality and costs in the long term. These are just some of the tools/techniques that could be beneficial in MT Deployment:
Cost-Benefit Analysis
This is very helpful in resource allocation. Conducting a cost-benefit analysis helps evaluate the effectiveness of improvement initiatives, ensuring that changes enhance quality without excessively increasing costs.
Value Engineering
This involves optimizing product value by minimising unnecessary costs while maintaining or improving quality. In localization, it means using the most cost-effective translation methods for each content type.
Quality Control Methods
Implementing statistical process control or Six Sigma methodologies can help monitor and continuously improve translation quality. The goal is to make sure that localized content meets high standards. See, for instance, this specialist’s breakdown on how to apply Lean Six Sigma in localization.
Source: Rauva.com
4. Leveraging Technology for Efficiency
Machine translation is not the only technology at play in localization. Aim to use technology to automate repetitive tasks and streamline processes. Here’s how some of them greatly aid MT implementation:
- Translation Memories: Use TMs with previously translated segments for consistency, reducing the need for duplicated efforts, and training MT engines.
- Automation and Integration: Other technologies can automate text extraction and integration tasks, allowing for smoother collaboration and faster project completion. Moreover, new integrations with LLMs can also offer new possibilities in terms of quality estimation and automated metrics.
Balancing cost and quality in machine translation deployment is achievable through strategic planning and the use of advanced technologies. Although some affirm that MT has replaced human translation, the best results in terms of quality and speed come from a good balance between humans and machines. Especially in creative areas where high volumes of content must be translated in record times, but outputs must still resonate with audiences culturally and language-wise.
Monitoring and improving workflows and the use of technology is a must to ensure implementation is achieving the desired results.
