Stock models are useful for translating texts that are simple and don’t feature technical or domain-specific terminology.
With custom models, the machine-translated output is more likely to be translated properly thanks to the language data the user provides. Terms and phrases that are more technical or domain-specific will be handled properly by a custom model compared to a stock model.
For most businesses and institutions, going the custom route is usually the optimal choice. It’s more expensive, but the returns in terms of quality and relevance are exponentially greater.
However, not all are able to make full use of custom machine latvia mobile database translation. This is especially the case when the user doesn’t have access to large amounts of language and translated data needed to customize an MT model properly. In this case, a better option would be to seek out MT providers that offer vertical stock models in the domain they need.
Stock models can be useful for general communications, but they fall short in many of the larger-scale use cases of machine translation.
Parting thoughts
As we mentioned earlier, choices like this come with tradeoffs. But there is a clear winner for most cases: custom models. Businesses and institutions looking to invest in machine translation would do well to go all-in if they have the means to do so.
Google’s Translation Hub puts professional-grade machine translation into business users’ hands
Google’s Translation Hub puts professional-grade machine translation into business users’ hands
A few weeks ago, at the opening keynote of the Google Cloud Next ‘22 event, Google CEO Sundar Pichai introduced Translation Hub, its new self-service translation document translation services platform that uses machine translation to translate large volumes of text into many different languages.