Gli output dei sistemi di traduzione automatica neurale: valutazione della qualità di Google Translate e DeepL Translator nella combinazione tedesco-italiano

MT is becoming a powerful tool for professional translators, language service providers and common users. The present work focuses on its quality, evaluating the translations produced by two neural MT systems – i.e. Google Translate and DeepL Translator – through manual error annotation. The data set used for this task is composed of semi-specialized German texts translated into Italian. Aim of the present work is to assess the quality of MT outputs for the data set considered and obtain a detailed overview of the type of errors made by the two neural MT systems examined. The first part of this work provides a theoretical background for MT and its evaluation. Chapter 1 deals with the definition of MT and summarizes its history. Moreover, a detailed analysis of the different MT architectures is provided, as well as an overview of the possible application scenarios and the different categories of users. Chapter 2 introduces the notion of quality in the translation field and the main automatic and manual methods applied to MT quality assessment tasks. A comprehensive analysis of some of the most significant studies on neural and phrase-based MT systems output quality is then provided. The second part of this work presents a quality assessment of the output produced by two neural MT systems, i.e. Google Translation and DeepL Translator. The evaluation was performed through manual error annotation based on a fine-grained error taxonomy. Chapter 3 outlines the methodology followed during the evaluation, with a description of the dataset, the neural MT systems chosen for the study, the annotation tool and the taxonomy used during the annotation task. Chapter 4 provides the results of the evaluation and a comment thereof, offering examples extracted from the annotated data set. The final part of this work summarizes the major findings of the present contribution. Results are then discussed, with a focus on their implication for future work.