8. Improvement of Chinese-to-English MT of Pharmaceutical Texts

In our last blog, we discussed how Chilin offered English-Chinese parallel sentence pairs on the TAUS Data Marketplace. Our first offerings are related to the Pharmaceutical-Biotechnology domain.

Does this data actually improve machine translation? The answer is “yes” as we show below.

Chilin conducted a test using the parallel sentence data and the AutoML Translation tool on the Google Cloud Platform. The study was based on:

8,000 English – Chinese sentence pairs for TRAINING. 
2,000 sentence pairs for VALIDATION.
2,000 sentence pairs for TRAINING.

Once the data is selected and loaded, AutoML makes it relatively simple to train a custom machine translation model. This simplicity is by design, as described in this interview with Francesco Bombassei of Google on Slator.com - Inside Google’s Custom Neural Machine Translation—AutoML Translate. “Under the hood, he explained that AutoML works with transfer learning and neural architecture search. Transfer learning is a way to use machine learning models as a basis for training others.” Domain specific training data is used to improve the base Google NMT system for that specific domain.

The Results: The Chilin Custom MT showed a BLEU score improvement of almost 5 points.

The testing was performed on patent based parallel sentences outside of the Training, Validation and Test sets. Here are some sample results:

English to Chinese Example #1

English to Chinese Example #2

English to Chinese Example #3

While similar, we have found that the enhanced model produces output that is more accurate and more readable, as one would expect with a 5-point BLEU score improvement.

Questions:

In the above three examples, which of the A, B, and C translations is the best?
Is human translation always the best, and error-free? We welcome your views
-Click Here-

If you would like details on the test and the results, contact us.

In our next blog, we will test a Chinese to English model.

L.Cady