To use an analogy, classifiers are like judges at an audition. These judges have built up experience over the years that qualifies them to assess audition performances based on certain criteria. These judges are then given the liberty to decide who meets the criteria to get through to the next round. Similarly, classifiers learn from training data in order to assess data points based on specific features. In the case of classifiers, they determine whether a text more closely meets the criteria for a human translation or a machine translation.
In their study, Automatic Discrimination of Human and Neural Machine Translation in Multilingual Scenarios,the University of Groningen’s research team assessed the performance of classifiers trained on monolingual language czech republic mobile database models and those trained on multilingual models (using English, German, Russian, and Chinese engines). But what types of classifiers did they use?
Note that a machine learning model can be trained to perform different types of classification tasks. It is important to understand that the types provided above only pertain to a certain algorithm/structure being utilized by a language model at any given time.
Want to learn more about models? Read about DeBERTa – the language model used by the researchers from University of Groningen.