How does one implement Machine Translation (MT) in their lives?
Here’s a general approach to MT selection which we mentioned before:
Looks good and clear. But let’s dig further into how this nice scheme can be applied to a Machine Translation Post Editing (MTPE) workflow. Here’s a common 4-step way:
- Preparation:
- Preparing the source data to train the engine
- Evaluating the market of best MT engines for your language pair
- Selecting engines for testing
- Testing and QA, which is normally done with the help of MT providers or external MT consultants
- Actual post-editing, which includes collecting feedback from translators on MT output quality
- Further QA, with evaluating the results and possibly retraining the engine
Then, depending on the project size, there may be variations:
| Small PEMT projects | Medium projects / MT as a first step | Major projects / MT only |
|---|---|---|
| 1. Prepare a set of segments for evaluation (about 1,500-2,000) | 1. Make a comparison of the basic stock engines: determine which maximum quality of translations can be obtained without training engines |
| 2. Select 4-5 engines to evaluate | 2. Prepare a list of stock engines for quality assessment per language pair | 2. Prepare a glossary of terms/phrases and a corpus for training (~10,000+ segments) |
3a. Plug the engines into a CAT tool | 3. Translate the test set of segments | 3. Train suitable domain adaptive engines |
3b. Translate materials through selected 4-5 engines and load into your CAT tool | 4. Calculate automatic reference-based coefficients | 4. Evaluate the quality of trained engines and compare with the base stock models
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| 4. Choose the best option during translation | 5. Compare the best engines according to the results of the coefficients and quality | 5. When you collect feedback from translators on quality, re-train your favorite engines and evaluate quality again
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| 6. Try translating through the winning engine |
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See also the state of MT by Intento for an in-depth guide on what solutions there are on the market.
