Detekce strojově přeložených textů pomocí strojového učení

Abstract

This thesis aims to describe and design a classifier for text data that can detect machine-translated texts using Google Translate and DeepL. This involves creating a custom dataset on which the models, of different architectures and modifications, will be taught and tested.

Description

Subject(s)

Machine learning, Neural Network, Transformer, LSTM, GRU, BERT, NLP, Semantic analysis, Dataset, Model

Citation