How to fast-track machine translation projects


Data is the lifeblood of any successful machine learning model, and machine translation models are no exception. Without relevant and properly labelled data, even the most sophisticated model will be unable to achieve reliable results.

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Defined Translation

 

Why Perfect Datasets Matter for Machine Translation 

 
In machine translation, the model learns patterns from the translation data. In particular, it learns how to translate text from the source to the target and how to arrange those words in a sentence to convey the appropriate meaning from the source sentence. 

What does that look like without a perfect dataset? Well, if you provide a machine translation engine with badly translated examples, those examples will have an impact on the resulting model. They will teach the model that those patterns are good translations, when in reality they are not. 

Go here to get ready-to-use translation training and testing data.

 

Imperfect Data Causes Mistranslations and Confusion 

 
There are two important angles to consider here. First, the model itself. For example, the model could mistranslate an idiomatic expression, having already learned a mistranslation during training. That would result in obviously machine-generated output, not the human translator level quality that’s required. 

Second, let’s consider the overall subject matter domain of the translation in question. Imagine a set of words that are used in a specific domain, such as news or parliamentary dialogue. Those domains use different sets of words to convey different types of meaning. To avoid confusion, it’s important not only to have accurate data, but also to have data that’s directly relevant, both to the subject matter and the wider domain that you wish to translate from. 

 

How to fast-track your machine translation projects

 
If you’re building, evaluating, or improving your machine translation engine, we can help you achieve your goals with high-quality, ready-to-use translation training and testing data.

Now you can fast-track your machine translation projects with 4 billion units of bilingual data in 40 languages (all translated from English) and 16 domains. With transparent quality metrics and tiered pricing based on quality type, training and testing your MT models has never been easier.

“When the market requires you to launch a feature fast, you cannot wait until your data collection is done. And let’s face it, often open-source data doesn’t cut it. Launch your Machine Translation project to market faster with our multi-lingual, ready-to-use data,” said Alessandro Giannetti, Director of Translation at Defined.ai.

Click here to get more information on the types of languages and domains available.



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