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.
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.
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
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