Machine Translation Model Based on Convolutional Neural Networks

Strojno prevođenje jedan je od središnjih i komercijalno najisplativijih zadataka obrade prirodnog jezika. Najnoviji modeli temeljeni na dubokom strojnom učenju, odnosno modeli neuronskog strojnog prevođenja, doveli su do značajnih napredaka u točnosti sustava strojnog prevođenja. Iako se modeli neuronskog strojnog prevođenja obično temelje na povratnim neuronskim mrežama, u okviru ovog rada izgrađen je model neuronskog strojnog prevođenja koji se u potpunosti temelji na konvolucijskim neuronskim mrežama. Nad izgrađenim modelom provedeno je eksperimentalno vrednovanje postupaka te je obavljena analiza dobivenih rezultata.Machine translation is one of the central and most commercially successful tasks of natural language processing. The latest models based on deep machine learning, i.e., neural machine translation models, have led to significant advances in the accuracy of the machine translation system. While neural machine translation models are usually based on recurrent neural networks, in this thesis, a model of neural machine translation was built that is entirely based on convolutional neural networks. The constructed model underwent an experimental process evaluation and an analysis of the obtained results was performed