Neural Machine Translation (NMT) has shown a promising performance on literary texts. Since the literary machine translation has not yet been researched for the English-to-Slovene translation direction, this Master’s thesis aims to bridge this gap by presenting a comparison among bespoke NMT models, tailored to novels, and Google Translate. To determine whether models, adapted to literary texts, perform better than Google Translate, and to establish whether an adaptation to a specific author further improves the performance of the NMT system, models were evaluated by the BLEU and METEOR metrics, error analysis of machine translation output, assessment of fluency and adequacy, and measurement of the post-editing (PE) effort. The findings show that all evaluated NMT approaches resulted in increases in translation productivity. The model, tailored to a specific author, performs better than the model, trained on a literary corpus, based on all scores except the scores for fluency. However, Google Translate still outperforms all bespoke models. The evaluation reveals a very low inter-rater agreement on fluency and adequacy, based on the kappa coefficient values, and significant discrepancies between post-editors. This suggests that these methods are not reliable, which should be addressed in future studies.
|
