Significant Improvements Observed on the Detection of Human vs Machine Translated Texts
Posted: Sat Feb 08, 2025 7:00 am
The numbers don’t lie: generative AI is not just a passing trend, but an integral part of our evolving digital landscape. Therefore, it’s essential to recognize its drawbacks, harness its power, and focus on the quality of your content. By following the tips and tricks provided, you can ensure that your content is the type sought out on the web and stands out among the vast sea of both human and machine-generated information.
The University of Groningen in the Netherlands – with study leads Malina Chichirau, Rik van Noord, and Antonio Toral – recently published a study that shows a significant improvement on classifiers discerning between czech republic mobile database human versus machine translation. The team used fine-tuned monolingual* and multilingual** language models for their classifiers – testing each model’s performance based on the quality of training data provided. Both types of models excelled when provided with training data from multiple source languages.
—-- *models that are only capable of processing one language;
** models that are capable of processing two or more languages
Due to advancements in AI, machine learning, and neural networks, the language industry has begun to assimilate machine translation into their daily operations. As such machine translation research is crucial, more so the understanding of the variables that are at the center of these studies. In this article we will dissect these variables (i.e., classifiers and training data) to gain appreciation for and have a better understanding of studies for machine translation/learning.
The University of Groningen in the Netherlands – with study leads Malina Chichirau, Rik van Noord, and Antonio Toral – recently published a study that shows a significant improvement on classifiers discerning between czech republic mobile database human versus machine translation. The team used fine-tuned monolingual* and multilingual** language models for their classifiers – testing each model’s performance based on the quality of training data provided. Both types of models excelled when provided with training data from multiple source languages.
—-- *models that are only capable of processing one language;
** models that are capable of processing two or more languages
Due to advancements in AI, machine learning, and neural networks, the language industry has begun to assimilate machine translation into their daily operations. As such machine translation research is crucial, more so the understanding of the variables that are at the center of these studies. In this article we will dissect these variables (i.e., classifiers and training data) to gain appreciation for and have a better understanding of studies for machine translation/learning.