Machine translation (MT) has proven to be an influential tool in overcoming language barriers, enhancing communication worldwide, and increasing efficiency across several industries. Not all machine translations, though, are flawless. Tailoring MT enables companies to personalize machine translations to fit their industry, language tone, and particular requirements.
In this blog, we’ll take you through the essential elements of MT customization, how it works, its benefits, and best practices for data preparation and training custom MT models.
Understanding MT Customization
Machine translation customization is the process of fine-tuning a machine translation system to meet the particular requirements of an industry, business, or language pair.
In contrast to general-purpose machine translation systems such as Google Translate, customized MT models are designed to capture particular terminology, style, and context applicable to the operations of a company.
This process improves translation quality, reduces the need for post-editing, and ensures that the translated content aligns more closely with the company’s preferred tone and terminology.
The Evolution of Machine Translation Customization
Machine translation has come a long way since its invention. The first techniques, which were called Rule-Based Machine Translation (RBMT), depended on pre-established linguistic rules, but the systems proved to be inflexible and prone to errors. Then came Statistical Machine Translation (SMT), which utilized vast sets of data to acquire language patterns, leading to improved translations.
Now, we have Neural Machine Translation (NMT), which is an advanced system utilizing deep learning for better translation accuracy. NMT models could be tailored by training them on certain data that identifies the needs of a business, and thus machine translation customization becomes more efficient and effective.
56% of the world’s companies already use or plan to use custom machine translation systems to enhance translation quality, as per a report by Common Sense Advisory.
Why MT Customization Matters
MT customization is essential for companies that require high-quality translations from specialized domains like law, medicine, technology, or marketing material. Generic MT systems often fail to grasp the nuances and terminology unique to these industries.
Significant Advantages of MT Customization:
- Better Translation Quality: Custom MT models comprehend specific terms, leading to better translations.
- Cost and Time Efficiency: Reducing the need for extensive post-editing saves both time and resources.
- Consistency: Custom models ensure consistency across translated documents, maintaining the same terminology and style throughout.
- Adaptability: Customized models can evolve with your business, learning from new data to improve over time.
Who Can Benefit from MT Customization?
The most common industries include:
| Industry | Why MT Customization Matters |
| Healthcare | Precise medical terminology and patient instructions are critical. |
| Legal | Legal language needs to be precise and legally sound. |
| E-commerce | Product descriptions and reviews need to be accurate across regions. |
| Finance | Financial documents require high accuracy to avoid misinterpretation. |
| Marketing | Creative, nuanced translations are needed for cultural relevance. |
For example, the healthcare industry often requires translations of complex medical records and patient documents. A customized MT model can be trained on specific medical terms to improve translation accuracy in this field.
Types of Machine Translation Customization
Three main types of customization are:
1. Terminology Customization
This type of customization involves training the MT model to recognize and properly translate specific industry terms or company jargon. For instance, a tech company may have its own unique product names and terminology that need to be translated consistently across all languages.
2. Style Customization
For businesses that require translations to capture a specific tone, style, or brand, the MT model can be tailored to provide translations that preserve these stylistic features. This is the case in marketing translation, where the emotion and tone of the message are just as critical as the content.
3. Domain-Specific Customization
This is done by training the MT model to work with content for a particular domain, like legal, financial, or medical translation. Domain-specific models are trained on a vast collection of documents related to the domain to enhance the accuracy and pertinence of the translations.
Preparing Your Data for MT Customization
Good-quality data is needed for effectively fine-tuning machine translation models. The quality of your data is directly related to how good the fine-tuned translations will be. These are some important steps for prepping your data:
1. Gathering Parallel Data
Parallel data involves source and target language pairs. Parallel data assists the MT model in learning what to do to translate material well. Providing more parallel data will allow the model to be trained more effectively.
2. Maintaining Data Quality
Your data must be clean and free of errors. Low-quality data can result in low-quality translation, so it’s crucial to check and clean the data prior to inputting it into the model.
3. Data Volume
The more data you can input, the better your model will work. But data also needs to be relevant to your industry or domain to have a significant effect on translation quality.
Best Practices for Cleaning Machine Translation Data
Cleaning your data is an essential step before training a custom MT model. Here are some best practices to ensure data quality:
| Practice | Description |
| Remove Duplicates | Ensure that there are no duplicate entries in your dataset. |
| Fix Inconsistencies | Address any inconsistencies in terminology or formatting. |
| Segment Data Properly | Break long texts into smaller, coherent segments for better learning. |
| Avoid Noisy Data | Eliminate irrelevant or incorrect data that could confuse the model. |
According to a report by Phrase, clean data can boost translation accuracy by up to 30%.
An Introduction to Training Custom MT Models
Once your data is ready, the next step is training your custom MT model. Training involves feeding the parallel data into the system so that it can learn how to generate better translations.
Key Steps in Training:
- Data Preprocessing: Preparing the data for the model.
- Model Selection: Choosing the appropriate neural network model for training.
- Training Process: Running the model on your data to learn patterns and improve translation.
Key Requirements for Training a Custom MT Model
For effective training, you need the following requirements:
- High-Quality Data: As mentioned earlier, clean and relevant data is key.
- Sufficient Data Volume: Large datasets provide the model with more examples to learn from.
- Computational Resources: Training models require powerful computational resources to process large amounts of data.
According to Microsoft Research, high-quality custom models can reduce translation errors by as much as 50% compared to generic MT systems.
Evaluating and Fine-Tuning Custom MT Models
After training, the custom MT model needs to be evaluated to ensure it meets your quality standards. Evaluation involves checking for accuracy, fluency, and consistency across different test translations.
Key Evaluation Metrics:
- BLEU Score: Measures the similarity between the machine translation and a human translation.
- Human Review: Conducting human evaluations to catch subtle errors.
Fine-tuning can further improve the model by adjusting certain parameters or feeding more data into the system.
The Final Step: Achieving Success with MT Customization
Once the MT model has been trained, evaluated, and fine-tuned, it’s ready for deployment. Over time, the model will continue to learn and improve as more data is provided.
Achieving success with machine translation customization requires careful planning, quality data, and ongoing improvements. When done correctly, customized MT models can significantly enhance translation quality and efficiency, saving businesses time and resources.
Related Articles:
– Machine Translation and Human Translation. Who is the winner?
– Foundational Insights of Machine Translation Post Editing (MTPE)
– MTPE: The Evolution of Translation Technology and What Lies Ahead